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2026
Conference article
Open Access
Advancements in Artificial Intelligence and Computer Vision for Biomedical Applications @SI-Lab
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Riccardo Del Gratta, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Barbara Leporini, Massimo Magrini, Massimo Martinelli, Davide Moroni, Ali Reza Omrani, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Laura Sebastiaini, Marco Tampucci, Valeria Tateo, Federico Volpini
This paper summarizes recent research at the Signals and Images Lab (ISTI-CNR) leveraging artificial intelligence, machine learning, and computer vision to address complex biomedical challenges. We highlight advancements in trustworthy, explainable algorithmic foundations alongside their practical clinical applications across diagnostic imaging, physiological signal analysis and neuromotor rehabilitation.
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Riccardo Del Gratta, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Barbara Leporini, Massimo Magrini, Massimo Martinelli, Davide Moroni, Ali Reza Omrani, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Laura Sebastiaini, Marco Tampucci, Valeria Tateo, Federico Volpini
This paper summarizes recent research at the Signals and Images Lab (ISTI-CNR) leveraging artificial intelligence, machine learning, and computer vision to address complex biomedical challenges. We highlight advancements in trustworthy, explainable algorithmic foundations alongside their practical clinical applications across diagnostic imaging, physiological signal analysis and neuromotor rehabilitation.
2025
Other
Open Access
SI-Lab Annual Research Report 2024
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
2025
Other
Open Access
ISTI-day 2025 Proceedings
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
See at:
CNR IRIS 
| www.isti.cnr.it | CNR IRIS 
2025
Journal article
Open Access
Topological machine learning for discriminative spectral band identification in raman spectroscopy of pathological samples
Conti F., Moroni D., Pascali M. A.
In the field of Raman spectroscopy (RS), particularly when working with biological samples, identifying the chemical compounds most involved in specific pathologies is of critical importance for pathologists. The correlation between chemical substances present in biological tissue and pathology can contribute not only to a deeper understanding of the disease itself but also to the development of novel artificial intelligence-based diagnostic methodologies. Motivated by these clinical challenges, we propose a method to identify the most discriminative spectral bands by leveraging the synergy between Topological Machine Learning (TML) and Raman spectroscopy. The intrinsic explainability of part of the TML pipeline can indeed play a key role in the detection of such spectral bands, e.g., the proteins most associated with the disease. In order to evaluate the performance of our method, we apply it to three case studies: the RS of biological tissue related to the chondrogenic bone tumors, the RS of cerebrospinal fluid associated with Alzheimer’s disease and the RS of pancreatic tissue. The results obtained with our method are promising in pinpointing which spectral bands are most relevant for diagnosis, but they also highlight the need for further investigation.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129053
Metrics:
Conti F., Moroni D., Pascali M. A.
In the field of Raman spectroscopy (RS), particularly when working with biological samples, identifying the chemical compounds most involved in specific pathologies is of critical importance for pathologists. The correlation between chemical substances present in biological tissue and pathology can contribute not only to a deeper understanding of the disease itself but also to the development of novel artificial intelligence-based diagnostic methodologies. Motivated by these clinical challenges, we propose a method to identify the most discriminative spectral bands by leveraging the synergy between Topological Machine Learning (TML) and Raman spectroscopy. The intrinsic explainability of part of the TML pipeline can indeed play a key role in the detection of such spectral bands, e.g., the proteins most associated with the disease. In order to evaluate the performance of our method, we apply it to three case studies: the RS of biological tissue related to the chondrogenic bone tumors, the RS of cerebrospinal fluid associated with Alzheimer’s disease and the RS of pancreatic tissue. The results obtained with our method are promising in pinpointing which spectral bands are most relevant for diagnosis, but they also highlight the need for further investigation.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129053
Metrics:
2025
Conference article
Open Access
Leveraging AI for Signal and Image Analysis in Medicine and Health
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Massimo Magrini, Davide Moroni, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Federico Volpini
The integration of artificial intelligence (AI) into the medical domain is driving innovation and progress in healthcare. This paper summarizes the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in several applications, e.g., in the analysis of biomedical data, and in the development of tools for enhancing trustworthiness and reliability of AI based systems. From cancer diagnosis and grading, to the analysis of body physiological signals to improve the understanding of dance movement therapy as an approach to healthy aging, this work highlights the paradigm shift that AI has brought into medicine and healthcare.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, June 23-24, 2025
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Massimo Magrini, Davide Moroni, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Federico Volpini
The integration of artificial intelligence (AI) into the medical domain is driving innovation and progress in healthcare. This paper summarizes the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in several applications, e.g., in the analysis of biomedical data, and in the development of tools for enhancing trustworthiness and reliability of AI based systems. From cancer diagnosis and grading, to the analysis of body physiological signals to improve the understanding of dance movement therapy as an approach to healthy aging, this work highlights the paradigm shift that AI has brought into medicine and healthcare.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, June 23-24, 2025
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Topological machine learning for Raman spectroscopy: perspectives for pancreatic diseases
Conti F., Lazzini G., Gaeta R., Pollina L. E., Comandatore A., Furbetta N., Morelli L., D'Acunto M., Moroni D., Pascali M. A.
The analysis of tissue samples from 17 subjects clinically diagnosed with chronic pancreatitis, ductal adenocarcinoma, or classified as controls has been collected and analyzed by Raman spectroscopy (RS). Such data are classified using a recent methodology which combines machine learning with advanced topological data analysis (TDA) techniques, known as topological machine learning (TML). A classification accuracy of 82% was achieved following a cross-validation scheme with patient stratification, suggesting that the combination of RS and topological data analysis holds significant potential for distinguishing between the three diagnostic categories. When restricted to binary classification (cancer vs. no cancer), performance increases to 88%. This approach offers a promising and fast method to support clinical diagnoses, potentially improving diagnostic accuracy and patient outcomes.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129061
Metrics:
Conti F., Lazzini G., Gaeta R., Pollina L. E., Comandatore A., Furbetta N., Morelli L., D'Acunto M., Moroni D., Pascali M. A.
The analysis of tissue samples from 17 subjects clinically diagnosed with chronic pancreatitis, ductal adenocarcinoma, or classified as controls has been collected and analyzed by Raman spectroscopy (RS). Such data are classified using a recent methodology which combines machine learning with advanced topological data analysis (TDA) techniques, known as topological machine learning (TML). A classification accuracy of 82% was achieved following a cross-validation scheme with patient stratification, suggesting that the combination of RS and topological data analysis holds significant potential for distinguishing between the three diagnostic categories. When restricted to binary classification (cancer vs. no cancer), performance increases to 88%. This approach offers a promising and fast method to support clinical diagnoses, potentially improving diagnostic accuracy and patient outcomes.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129061
Metrics:
See at:
CNR IRIS | www.mdpi.com | CNR IRIS
2024
Conference article
Open Access
Advancing sustainability: research initiatives at the Signals and Images Lab
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Harnessing topological machine learning in Raman spectroscopy: perspectives for Alzheimer’s disease detection via cerebrospinal fluid analysis
Conti F., Banchelli M., Bessi V., Cecchi C., Chiti F., Colantonio S., D'Andrea C., De Angelis M., Moroni D., Nacmias B., Pascali M. A., Sorbi S., Matteini P.
The cerebrospinal fluid of 21 subjects who received a clinical diagnosis of Alzheimer’s disease (AD) as well as of 22 pathological controls has been collected and analysed by Raman spectroscopy (RS). We investigated whether the Raman spectra could be used to distinguish AD from controls, after a preprocessing procedure. We applied machine learning to a set of topological descriptors extracted from the spectra, achieving a high classification accuracy of 86%. Our experimentation indicates that RS and topological analysis may be a reliable and effective combination to confirm or disprove a clinical diagnosis of Alzheimer’s disease. The following steps will aim at leveraging the intrinsic interpretability of the topological data analysis to characterize the AD subtypes, e.g. by identifying the bands of the Raman spectrum relevant for AD detection, possibly increasing and/or confirming the knowledge about the precise molecular events and biological pathways behind the Alzheimer’s disease.Source: JOURNAL OF THE FRANKLIN INSTITUTE, vol. 361 (issue 18)
DOI: 10.1016/j.jfranklin.2024.107249
Project(s): Proteomics, RAdiomics & Machine learning-integrated strategy for precision medicine for Alzheimer’s
Metrics:
Conti F., Banchelli M., Bessi V., Cecchi C., Chiti F., Colantonio S., D'Andrea C., De Angelis M., Moroni D., Nacmias B., Pascali M. A., Sorbi S., Matteini P.
The cerebrospinal fluid of 21 subjects who received a clinical diagnosis of Alzheimer’s disease (AD) as well as of 22 pathological controls has been collected and analysed by Raman spectroscopy (RS). We investigated whether the Raman spectra could be used to distinguish AD from controls, after a preprocessing procedure. We applied machine learning to a set of topological descriptors extracted from the spectra, achieving a high classification accuracy of 86%. Our experimentation indicates that RS and topological analysis may be a reliable and effective combination to confirm or disprove a clinical diagnosis of Alzheimer’s disease. The following steps will aim at leveraging the intrinsic interpretability of the topological data analysis to characterize the AD subtypes, e.g. by identifying the bands of the Raman spectrum relevant for AD detection, possibly increasing and/or confirming the knowledge about the precise molecular events and biological pathways behind the Alzheimer’s disease.Source: JOURNAL OF THE FRANKLIN INSTITUTE, vol. 361 (issue 18)
DOI: 10.1016/j.jfranklin.2024.107249
Project(s): Proteomics, RAdiomics & Machine learning-integrated strategy for precision medicine for Alzheimer’s
Metrics:
See at:
IRIS Cnr | IRIS Cnr | IRIS Cnr | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
From Covid-19 detection to cancer grading: how medical-AI is boosting clinical diagnostics and may improve treatment
Berti A., Buongiorno R., Carloni G., Caudai C., Conti F., Del Corso G., Germanese D., Moroni D., Pachetti E., Pascali M. A., Colantonio S.
The integration of artificial intelligence (AI) into medical imaging has guided an era of transformation in healthcare. This paper presents the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in medical imaging. From the convolutional neural network-based segmentation of Covid-19 lung patterns to the radiomic signature for benign/malignant breast nodule discrimination, to the automatic grading of prostate cancer, this work highlights the paradigm shift that AI has brought to medical imaging, revolutionizing diagnosis and patient care.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 336-341. Naples, Italy, 29-30/05/2024
Berti A., Buongiorno R., Carloni G., Caudai C., Conti F., Del Corso G., Germanese D., Moroni D., Pachetti E., Pascali M. A., Colantonio S.
The integration of artificial intelligence (AI) into medical imaging has guided an era of transformation in healthcare. This paper presents the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in medical imaging. From the convolutional neural network-based segmentation of Covid-19 lung patterns to the radiomic signature for benign/malignant breast nodule discrimination, to the automatic grading of prostate cancer, this work highlights the paradigm shift that AI has brought to medical imaging, revolutionizing diagnosis and patient care.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 336-341. Naples, Italy, 29-30/05/2024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Drivers of vascular plant, bryophyte and lichen richness in grasslands along a precipitation gradient (central Apennines, Italy)
Cancellieri L., Sperandii M. G., Rosati L., Bellisario B., Franceschini C., Aleffi M., Bartolucci F., Becker T., Belonovskaya E., Berastegi A., Biurrun I., Brunetti M., Buckle C., Che R., Conti F., Dembicz I., Fanni S., Fantinato E., Frank D., Frattaroli A. R., Garcia-Mijangos I., Guglielmino A., Janisova M., Maestri S., Magnes M., Potenza G., Primi R., Sobolev N., Tsarevskaya N., Vacca A., Dengler J., Filibeck G.
Questions: Semi-natural grasslands in Southern Europe are biodiversity hotspots, yet their patterns of plant species richness are less studied than in Central Europe. In the Central Apennines (Italy), there are large areas of dry calcareous grasslands, across a steep gradient of mean annual precipitation (from 650 to 1350 mm within c. 30 km). We asked: How do these grasslands compare to other Palaearctic grasslands in richness levels? How do the precipitation gradient and other environmental predictors influence species richness? Does this influence differ among taxonomic groups?. Location: Submontane and lower-montane belt of the Central Apennines (Abruzzo and Lazio, Italy). Methods: We recorded the species richness of vascular plants and (terricolous) bryophytes and lichens in 97 plots of 10 m2, aligning them with the precipitation gradient while maintaining geological substrate and elevation similar. Mean temperature and precipitation were estimated with a high-resolution regional model. A wide array of environmental variables (including soil properties and grazing load) were measured for each plot. Multivariate relationships within and between response and predictor variables were studied with Canonical Correlation. The relative importance of predictors on response variables was modeled with Boosted Regression Trees. Results: The sampled grasslands were very species-rich in the Palaearctic context. Vascular plant richness was negatively influenced by topographic heat load and soil sand content, but we did not detect a relationship with mean annual precipitation. Bryophyte richness was poorly modeled by the measured variables, although it was positively correlated with lichen richness. Lichen richness had a marked negative relationship with soil phosphorus and mean annual precipitation. Conclusions: In Southern European semi-natural mountain grasslands, vascular plant richness is driven more by fine-scale edaphic factors than by precipitation gradients. In contrast, bryophyte and lichen species richness is predicted by a mixture of climatic and edaphic variables.Source: JOURNAL OF VEGETATION SCIENCE, vol. 35 (issue 5)
DOI: 10.1111/jvs.13305
Metrics:
Cancellieri L., Sperandii M. G., Rosati L., Bellisario B., Franceschini C., Aleffi M., Bartolucci F., Becker T., Belonovskaya E., Berastegi A., Biurrun I., Brunetti M., Buckle C., Che R., Conti F., Dembicz I., Fanni S., Fantinato E., Frank D., Frattaroli A. R., Garcia-Mijangos I., Guglielmino A., Janisova M., Maestri S., Magnes M., Potenza G., Primi R., Sobolev N., Tsarevskaya N., Vacca A., Dengler J., Filibeck G.
Questions: Semi-natural grasslands in Southern Europe are biodiversity hotspots, yet their patterns of plant species richness are less studied than in Central Europe. In the Central Apennines (Italy), there are large areas of dry calcareous grasslands, across a steep gradient of mean annual precipitation (from 650 to 1350 mm within c. 30 km). We asked: How do these grasslands compare to other Palaearctic grasslands in richness levels? How do the precipitation gradient and other environmental predictors influence species richness? Does this influence differ among taxonomic groups?. Location: Submontane and lower-montane belt of the Central Apennines (Abruzzo and Lazio, Italy). Methods: We recorded the species richness of vascular plants and (terricolous) bryophytes and lichens in 97 plots of 10 m2, aligning them with the precipitation gradient while maintaining geological substrate and elevation similar. Mean temperature and precipitation were estimated with a high-resolution regional model. A wide array of environmental variables (including soil properties and grazing load) were measured for each plot. Multivariate relationships within and between response and predictor variables were studied with Canonical Correlation. The relative importance of predictors on response variables was modeled with Boosted Regression Trees. Results: The sampled grasslands were very species-rich in the Palaearctic context. Vascular plant richness was negatively influenced by topographic heat load and soil sand content, but we did not detect a relationship with mean annual precipitation. Bryophyte richness was poorly modeled by the measured variables, although it was positively correlated with lichen richness. Lichen richness had a marked negative relationship with soil phosphorus and mean annual precipitation. Conclusions: In Southern European semi-natural mountain grasslands, vascular plant richness is driven more by fine-scale edaphic factors than by precipitation gradients. In contrast, bryophyte and lichen species richness is predicted by a mixture of climatic and edaphic variables.Source: JOURNAL OF VEGETATION SCIENCE, vol. 35 (issue 5)
DOI: 10.1111/jvs.13305
Metrics:
See at:
Archivio istituzionale della ricerca - Università di Camerino | Archivio istituzionale della ricerca - Università degli Studi di Venezia Ca' Foscari | Archivio istituzionale della ricerca - Università di Cagliari | CNR IRIS | Archivio istituzionale della ricerca - Università di Camerino | Archivio istituzionale della ricerca - Università di Camerino | CNR IRIS
2024
Other
Open Access
A bridge between persistent homology and group equivariant non-expansive operators: theory and applications
Conti F., Frosini P., Moroni D., Pascali M. A.
Topological Data Analysis (TDA) is proving to be an excellent tool for shape analysis of digital data. The recently found synergy with artificial intelligence gave rise to Topological Machine Learning (TML), which aims to combine the expressive power of computational topology with the accuracy of machine learning to provide a comprehensive and automatic framework for data classification. The aim of this thesis is twofold: to develop current applications of TML in practical scenarios, with emphasis on the most overlooked aspects of its pipeline, and to connect the theory of TDA with a broader class of maps, the Group Equivariant Non-Expansive Operators (GENEOs). In the first part of this dissertation, we develop a pipeline to study digital data by means of TML in order to validate the practical aspects of our theory. We apply this pipeline to benchmark and experimental datasets, achieving state-of-the-art accuracies in biomedical scenarios. Moreover, we perform an empirical but extensive study of the stability of features arising from the various homological dimensions with respect to noise and points distribution in the persistence diagram. Such a comparison is novel in the TML literature and our findings show that results coming from the concatenation of each homological dimension available are the best approach in the vectorization step. We later expand on the main concept of TDA, proving that the functor that computes persistence diagrams can be seen as a particular instance of GENEOs (Theorem 4.1.4). The GENEO framework allows us to inject arbitrary equivariances in a machine learning setting and represents a new possible approach to neural network architecture. Next, we fully present the theory of GENEOs and their properties, such as convexity and concavity, under suitable assumptions. This thesis expand the GENEO theory with two new tools to define such operators, namely using symmetric functions (Theorem 5.3.24) and a characterization theorem of linear GENEOs between arbitrary functional spaces (Theorem 6.2.2). Finally, we develop a new neural network architecture with GENEOs instead of neurons and show its potential in a couple of applications.
Conti F., Frosini P., Moroni D., Pascali M. A.
Topological Data Analysis (TDA) is proving to be an excellent tool for shape analysis of digital data. The recently found synergy with artificial intelligence gave rise to Topological Machine Learning (TML), which aims to combine the expressive power of computational topology with the accuracy of machine learning to provide a comprehensive and automatic framework for data classification. The aim of this thesis is twofold: to develop current applications of TML in practical scenarios, with emphasis on the most overlooked aspects of its pipeline, and to connect the theory of TDA with a broader class of maps, the Group Equivariant Non-Expansive Operators (GENEOs). In the first part of this dissertation, we develop a pipeline to study digital data by means of TML in order to validate the practical aspects of our theory. We apply this pipeline to benchmark and experimental datasets, achieving state-of-the-art accuracies in biomedical scenarios. Moreover, we perform an empirical but extensive study of the stability of features arising from the various homological dimensions with respect to noise and points distribution in the persistence diagram. Such a comparison is novel in the TML literature and our findings show that results coming from the concatenation of each homological dimension available are the best approach in the vectorization step. We later expand on the main concept of TDA, proving that the functor that computes persistence diagrams can be seen as a particular instance of GENEOs (Theorem 4.1.4). The GENEO framework allows us to inject arbitrary equivariances in a machine learning setting and represents a new possible approach to neural network architecture. Next, we fully present the theory of GENEOs and their properties, such as convexity and concavity, under suitable assumptions. This thesis expand the GENEO theory with two new tools to define such operators, namely using symmetric functions (Theorem 5.3.24) and a characterization theorem of linear GENEOs between arbitrary functional spaces (Theorem 6.2.2). Finally, we develop a new neural network architecture with GENEOs instead of neurons and show its potential in a couple of applications.
2023
Journal article
Open Access
Raman spectroscopy and topological machine learning for cancer grading
Conti F, D'Acunto M, Caudai C, Colantonio C, Gaeta R, Moroni D, Pascali Ma
In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.Source: SCIENTIFIC REPORTS, vol. 13
DOI: 10.1038/s41598-023-34457-5
Metrics:
Conti F, D'Acunto M, Caudai C, Colantonio C, Gaeta R, Moroni D, Pascali Ma
In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.Source: SCIENTIFIC REPORTS, vol. 13
DOI: 10.1038/s41598-023-34457-5
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CNR IRIS | ISTI Repository | www.nature.com | CNR IRIS
2023
Conference article
Open Access
Analysis of sea surface temperature maps via topological machine learning
Conti F, Papini O, Moroni D, Pieri G, Reggiannini M, Pascali M A
Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.DOI: 10.1109/itnt57377.2023.10139044
Project(s): NAUTILOS
Metrics:
Conti F, Papini O, Moroni D, Pieri G, Reggiannini M, Pascali M A
Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.DOI: 10.1109/itnt57377.2023.10139044
Project(s): NAUTILOS
Metrics:
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2023
Other
Open Access
Alzheimer disease detection from Raman spectroscopy of the cerebrospinal fluid via topological machine learning
Conti F, Banchelli M, Bessi V, Cecchi C, Chiti F, Colantonio S, D'Andrea C, De Angelis M, Moroni D, Nacmias B, Pascali Ma, Sorbi S, Matteini P
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls have been collected and analysed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (> 87%). Although our results are preliminary, they indicate that RS and topological analysis together may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps will include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to understand if topological data analysis could support the characterization of AD subtypes.
Conti F, Banchelli M, Bessi V, Cecchi C, Chiti F, Colantonio S, D'Andrea C, De Angelis M, Moroni D, Nacmias B, Pascali Ma, Sorbi S, Matteini P
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls have been collected and analysed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (> 87%). Although our results are preliminary, they indicate that RS and topological analysis together may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps will include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to understand if topological data analysis could support the characterization of AD subtypes.
2023
Journal article
Open Access
Alzheimer disease detection from Raman spectroscopy of the cerebrospinal fluid via topological machine learning
Conti F, Banchelli M, Bessi V, Cecchi C, Chiti F, Colantonio S, D'Andrea C, De Angelis M, Moroni D, Nacmias B, Pascali Ma, Sorbi S, Matteini P
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls was collected and analyzed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (>87%). Although our results are preliminary, they indicate that RS and topological analysis may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to investigate whether topological data analysis could support the characterization of AD subtypes.Source: ENGINEERING PROCEEDINGS, vol. 51 (issue 1)
DOI: 10.3390/engproc2023051014
Metrics:
Conti F, Banchelli M, Bessi V, Cecchi C, Chiti F, Colantonio S, D'Andrea C, De Angelis M, Moroni D, Nacmias B, Pascali Ma, Sorbi S, Matteini P
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls was collected and analyzed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (>87%). Although our results are preliminary, they indicate that RS and topological analysis may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to investigate whether topological data analysis could support the characterization of AD subtypes.Source: ENGINEERING PROCEEDINGS, vol. 51 (issue 1)
DOI: 10.3390/engproc2023051014
Metrics:
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CNR IRIS | www.mdpi.com | CNR IRIS | CNR IRIS
2022
Contribution to conference
Open Access
A topological pipeline for machine learning
Conti F
The development of a topological pipeline for machine learning involves two crucial steps that strongly influence the performance of the pipeline. The first step is the choice of the filtration that associates a persistence diagram with digital data. The second step is the choice of the representation method for the persistence diagrams, which often relies on several parameters. In this work we develop a pipeline that associates persistence diagrams to digital data, via the most appropriate filtration for the type of data considered. Using a grid search approach, this pipeline determines optimal representation methods and parameters. We assess the performance of our pipeline, and in parallel we compare the different representation methods, on popular benchmark datasets. This work is a first step towards both an easy, ready to use, pipeline for data classification using persistent homology and machine learning, and to understand the theoretical reasons why, given a dataset and a task to be performed, a pair (filtration, topological representation) is better than another.
Conti F
The development of a topological pipeline for machine learning involves two crucial steps that strongly influence the performance of the pipeline. The first step is the choice of the filtration that associates a persistence diagram with digital data. The second step is the choice of the representation method for the persistence diagrams, which often relies on several parameters. In this work we develop a pipeline that associates persistence diagrams to digital data, via the most appropriate filtration for the type of data considered. Using a grid search approach, this pipeline determines optimal representation methods and parameters. We assess the performance of our pipeline, and in parallel we compare the different representation methods, on popular benchmark datasets. This work is a first step towards both an easy, ready to use, pipeline for data classification using persistent homology and machine learning, and to understand the theoretical reasons why, given a dataset and a task to be performed, a pair (filtration, topological representation) is better than another.
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CNR IRIS | ISTI Repository | sites.google.com | CNR IRIS
2022
Journal article
Open Access
On the construction of group equivariant non-expansive operators via permutants and symmetric functions
Conti F, Frosini P, Quercioli N
Group Equivariant Operators (GEOs) are a fundamental tool in the research on neural networks, since they make available a new kind of geometric knowledge engineering for deep learning, which can exploit symmetries in artificial intelligence and reduce the number of parameters required in the learning process. In this paper we introduce a new method to build non-linear GEOs and non-linear Group Equivariant Non-Expansive Operators (GENEOs), based on the concepts of symmetric function and permutant. This method is particularly interesting because of the good theoretical properties of GENEOs and the ease of use of permutants to build equivariant operators, compared to the direct use of the equivariance groups we are interested in. In our paper, we prove that the technique we propose works for any symmetric function, and benefits from the approximability of continuous symmetric functions by symmetric polynomials. A possible use in Topological Data Analysis of the GENEOs obtained by this new method is illustrated.Source: FRONTIERS IN ARTIFICIAL INTELLIGENCE, vol. 5
DOI: 10.3389/frai.2022.786091
Metrics:
Conti F, Frosini P, Quercioli N
Group Equivariant Operators (GEOs) are a fundamental tool in the research on neural networks, since they make available a new kind of geometric knowledge engineering for deep learning, which can exploit symmetries in artificial intelligence and reduce the number of parameters required in the learning process. In this paper we introduce a new method to build non-linear GEOs and non-linear Group Equivariant Non-Expansive Operators (GENEOs), based on the concepts of symmetric function and permutant. This method is particularly interesting because of the good theoretical properties of GENEOs and the ease of use of permutants to build equivariant operators, compared to the direct use of the equivariance groups we are interested in. In our paper, we prove that the technique we propose works for any symmetric function, and benefits from the approximability of continuous symmetric functions by symmetric polynomials. A possible use in Topological Data Analysis of the GENEOs obtained by this new method is illustrated.Source: FRONTIERS IN ARTIFICIAL INTELLIGENCE, vol. 5
DOI: 10.3389/frai.2022.786091
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CNR IRIS | ISTI Repository | www.frontiersin.org | CNR IRIS
2022
Journal article
Open Access
A topological machine learning pipeline for classification
Conti F, Moroni D, Pascali Ma
In this work, we develop a pipeline that associates Persistence Diagrams to digital data via the most appropriate filtration for the type of data considered. Using a grid search approach, this pipeline determines optimal representation methods and parameters. The development of such a topological pipeline for Machine Learning involves two crucial steps that strongly affect its performance: firstly, digital data must be represented as an algebraic object with a proper associated filtration in order to compute its topological summary, the Persistence Diagram. Secondly, the persistence diagram must be transformed with suitable representation methods in order to be introduced in a Machine Learning algorithm. We assess the performance of our pipeline, and in parallel, we compare the different representation methods on popular benchmark datasets. This work is a first step toward both an easy and ready-to-use pipeline for data classification using persistent homology and Machine Learning, and to understand the theoretical reasons why, given a dataset and a task to be performed, a pair (filtration, topological representation) is better than another.Source: MATHEMATICS, vol. 10 (issue 17)
DOI: 10.3390/math10173086
Metrics:
Conti F, Moroni D, Pascali Ma
In this work, we develop a pipeline that associates Persistence Diagrams to digital data via the most appropriate filtration for the type of data considered. Using a grid search approach, this pipeline determines optimal representation methods and parameters. The development of such a topological pipeline for Machine Learning involves two crucial steps that strongly affect its performance: firstly, digital data must be represented as an algebraic object with a proper associated filtration in order to compute its topological summary, the Persistence Diagram. Secondly, the persistence diagram must be transformed with suitable representation methods in order to be introduced in a Machine Learning algorithm. We assess the performance of our pipeline, and in parallel, we compare the different representation methods on popular benchmark datasets. This work is a first step toward both an easy and ready-to-use pipeline for data classification using persistent homology and Machine Learning, and to understand the theoretical reasons why, given a dataset and a task to be performed, a pair (filtration, topological representation) is better than another.Source: MATHEMATICS, vol. 10 (issue 17)
DOI: 10.3390/math10173086
Metrics:
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CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
2022
Other
Open Access
SI-Lab annual research report 2021
Righi M, Leone G R, Carboni A, Caudai C, Colantonio S, Kuruoglu E E, Leporini B, Magrini M, Paradisi P, Pascali M A, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Berti A, Bruno A, Buongiorno R, Carloni G, Conti F, Germanese D, Ignesti G, Matarese F, Omrani A, Pachetti E, Papini O, Benassi A, Bertini G, Coltelli P, Tarabella L, Straface S, Salvetti O, Moroni D
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.DOI: 10.32079/isti-ar-2022/003
DOI: https://doi.org/10.32079/isti-ar-2022/003
Metrics:
Righi M, Leone G R, Carboni A, Caudai C, Colantonio S, Kuruoglu E E, Leporini B, Magrini M, Paradisi P, Pascali M A, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Berti A, Bruno A, Buongiorno R, Carloni G, Conti F, Germanese D, Ignesti G, Matarese F, Omrani A, Pachetti E, Papini O, Benassi A, Bertini G, Coltelli P, Tarabella L, Straface S, Salvetti O, Moroni D
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.DOI: 10.32079/isti-ar-2022/003
DOI: https://doi.org/10.32079/isti-ar-2022/003
Metrics:
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CNR IRIS | ISTI Repository | CNR IRIS