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2023
Journal article
Open Access
Efficient adaptive ensembling for image classification
Bruno A., Moroni D., Martinelli M.
In recent times, except for sporadic cases, the trend in Computer Vision is to achieve minor improvements over considerable increases in complexity. To reverse this tendency, we propose a novel method to boost image classification performances without an increase in complexity. To this end, we revisited ensembling, a powerful approach, not often adequately used due to its nature of increased complexity and training time, making it viable by specific design choices. First, we trained end-to-end two EfficientNet-b0 models (known to be the architecture with the best overall accuracy/complexity trade-off in image classification) on disjoint subsets of data (i.e. bagging). Then, we made an efficient adaptive ensemble by performing fine-tuning of a trainable combination layer. In this way, we were able to outperform the state-of-the-art by an average of 0.5\% on the accuracy with restrained complexity both in terms of number of parameters (by 5-60 times), and FLoating point Operations Per Second (by 10-100 times) on several major benchmark datasets, fully embracing the green AI.Source: Expert systems (Online) (2023). doi:10.1111/exsy.13424
DOI: 10.1111/exsy.13424
Metrics:
Bruno A., Moroni D., Martinelli M.
In recent times, except for sporadic cases, the trend in Computer Vision is to achieve minor improvements over considerable increases in complexity. To reverse this tendency, we propose a novel method to boost image classification performances without an increase in complexity. To this end, we revisited ensembling, a powerful approach, not often adequately used due to its nature of increased complexity and training time, making it viable by specific design choices. First, we trained end-to-end two EfficientNet-b0 models (known to be the architecture with the best overall accuracy/complexity trade-off in image classification) on disjoint subsets of data (i.e. bagging). Then, we made an efficient adaptive ensemble by performing fine-tuning of a trainable combination layer. In this way, we were able to outperform the state-of-the-art by an average of 0.5\% on the accuracy with restrained complexity both in terms of number of parameters (by 5-60 times), and FLoating point Operations Per Second (by 10-100 times) on several major benchmark datasets, fully embracing the green AI.Source: Expert systems (Online) (2023). doi:10.1111/exsy.13424
DOI: 10.1111/exsy.13424
Metrics:
See at:
onlinelibrary.wiley.com 
| ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Revisiting ensembling for improving the performance of deep learning models
Bruno A., Moroni D., Martinelli M.
Ensembling is a very well-known strategy consisting in fusing several different models to achieve a new model for classification or regression tasks. Over the years, ensembling has been proven to provide superior performance in various contexts related to pattern recognition and artificial intelligence. Moreover, the basic ideas that are at the basis of ensembling have been a source of inspiration for the design of the most recent deep learning architectures. Indeed, a close analysis of those architectures shows that some connections among layers and groups of layers achieve effects similar to those obtainable by bagging, boosting and stacking, which are the well-known three basic approaches to ensembling. However, we argue that research has not fully leveraged the potential offered by ensembling. Indeed, this paper investigates some possible approaches to the combination of weak learners, or sub-components of weak learners, in the context of bagging. Based on previous results obtained in specific domains, we extend the approach to a reference dataset obtaining encouraging results.Source: ICPR 2022 - International Conference on Pattern Recognition, pp. 445–452, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37742-6_34
Metrics:
Bruno A., Moroni D., Martinelli M.
Ensembling is a very well-known strategy consisting in fusing several different models to achieve a new model for classification or regression tasks. Over the years, ensembling has been proven to provide superior performance in various contexts related to pattern recognition and artificial intelligence. Moreover, the basic ideas that are at the basis of ensembling have been a source of inspiration for the design of the most recent deep learning architectures. Indeed, a close analysis of those architectures shows that some connections among layers and groups of layers achieve effects similar to those obtainable by bagging, boosting and stacking, which are the well-known three basic approaches to ensembling. However, we argue that research has not fully leveraged the potential offered by ensembling. Indeed, this paper investigates some possible approaches to the combination of weak learners, or sub-components of weak learners, in the context of bagging. Based on previous results obtained in specific domains, we extend the approach to a reference dataset obtaining encouraging results.Source: ICPR 2022 - International Conference on Pattern Recognition, pp. 445–452, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37742-6_34
Metrics:
See at:
ISTI Repository | link.springer.com 
| CNR ExploRA
2023
Dataset
Unknown
A phenotyping weeds image dataset for open scientific research
Dainelli R., Martinelli M., Bruno A., Moroni D., Morelli S., Silvestri M., Ferrari E., Rocchi L., Toscano P.
This in-house-built image dataset consists of 10810 weed images captured through a dedicated phenotyping activity in quasi-field conditions. The targets are seven of the most widespread and hard-to-control weeds in wheat (but also in other winter cereals) in the Mediterranean environment. In the framework of open scientific research, our aim is to share low-cost and high-resolution images representing challenging agricultural environments where weather, lighting and other factors can change by the hour and affect the quality of images. This way the dataset could be used to train Artificial Intelligence architectures designed for weed recognition, allowing the implementation of tools directly available in the field for farmers and technicians for effective and timely weed management. The dataset encompasses weed images ranging from the post-emergence phase (i.e. the complete cotyledons unfolding) until the pre-flowering stage. The weed selection was made by considering (i) bottom-up information and specific requests by farmers and technicians, (ii) weed susceptibility to commercial formulations for chemical control <50%, reported at least twice by field technicians, (iii) the difficulty of control considering any methods, and (iv) the type of growing season (overlapping or not with wheat). The final weeds selection encompassed both monocots (Avena sterilis and Lolium multiflorum) and dicots (Convolvulus arvensis, Fumaria officinalis, Papaver rhoeas, Veronica persica and Vicia sativa). Image acquisition was facilitated by using a white panel as a background; this helped to (i) spread the light and thereby make the plants well-illuminated, while still avoiding strong shadows when using the flash and (ii) simplify image processing. The images were acquired with a Canon EOS 700D hand-held camera set in the macro mode with aperture, shutter speed, ISO and flash in auto mode. Photo capture timing, target distances and light conditions did not have a fixed pattern but were deliberately programmed to vary in such a way as to mimic field conditions. For image shooting at various times of the day, the only precaution was to frame the subject with homogeneous light conditions (full sunlight/full shade). The varied outdoor conditions (light, distance, timing) and camera type (RGB) with auto mode were essential features to make the images photos look similar to those that a user can take in a field, for example with a smartphone camera. After selection and categorization, images were cropped to select the region of interest following the 1:1 ratio but maintaining a minimum size of 512 x 512 pixels. More details on the dataset and its use for weed recognition tasks will be soon available in the proceedings of the forthcoming ECPA conference (2-6 July 2023, Bologna, Italy).
Dainelli R., Martinelli M., Bruno A., Moroni D., Morelli S., Silvestri M., Ferrari E., Rocchi L., Toscano P.
This in-house-built image dataset consists of 10810 weed images captured through a dedicated phenotyping activity in quasi-field conditions. The targets are seven of the most widespread and hard-to-control weeds in wheat (but also in other winter cereals) in the Mediterranean environment. In the framework of open scientific research, our aim is to share low-cost and high-resolution images representing challenging agricultural environments where weather, lighting and other factors can change by the hour and affect the quality of images. This way the dataset could be used to train Artificial Intelligence architectures designed for weed recognition, allowing the implementation of tools directly available in the field for farmers and technicians for effective and timely weed management. The dataset encompasses weed images ranging from the post-emergence phase (i.e. the complete cotyledons unfolding) until the pre-flowering stage. The weed selection was made by considering (i) bottom-up information and specific requests by farmers and technicians, (ii) weed susceptibility to commercial formulations for chemical control <50%, reported at least twice by field technicians, (iii) the difficulty of control considering any methods, and (iv) the type of growing season (overlapping or not with wheat). The final weeds selection encompassed both monocots (Avena sterilis and Lolium multiflorum) and dicots (Convolvulus arvensis, Fumaria officinalis, Papaver rhoeas, Veronica persica and Vicia sativa). Image acquisition was facilitated by using a white panel as a background; this helped to (i) spread the light and thereby make the plants well-illuminated, while still avoiding strong shadows when using the flash and (ii) simplify image processing. The images were acquired with a Canon EOS 700D hand-held camera set in the macro mode with aperture, shutter speed, ISO and flash in auto mode. Photo capture timing, target distances and light conditions did not have a fixed pattern but were deliberately programmed to vary in such a way as to mimic field conditions. For image shooting at various times of the day, the only precaution was to frame the subject with homogeneous light conditions (full sunlight/full shade). The varied outdoor conditions (light, distance, timing) and camera type (RGB) with auto mode were essential features to make the images photos look similar to those that a user can take in a field, for example with a smartphone camera. After selection and categorization, images were cropped to select the region of interest following the 1:1 ratio but maintaining a minimum size of 512 x 512 pixels. More details on the dataset and its use for weed recognition tasks will be soon available in the proceedings of the forthcoming ECPA conference (2-6 July 2023, Bologna, Italy).
See at: CNR ExploRA | zenodo.org
2023
Conference article
Closed Access
Recognition of weeds in cereals using AI architecture
Dainelli R., Martinelli M., Bruno A., Moroni D., Morelli S., Silvestri M., Ferrari E., Rocchi L., Toscano P.
In this study, an automatic system based on open AI architectures was developed and fed with an in-house built image dataset to recognize seven of the most widespread and hard-to-control weeds in wheat in the Mediterranean environment. A total of 10810 images were collected from the post-emergence (S1 dataset) to the pre-flowering stage (S2 dataset). A selection of pictures available from online sources (S3, 825 images) was used as a final and further independent test of the proposed recognition tool. The AI tool in the ensemble configuration achieved 100% accuracy on the validation and test set both for S1 and S2, while for S3 an accuracy of approximately 70% was achieved for weed species in the post-emergence stage.Source: ECPA 2023 - The 14th European Conference on Precision Agriculture - Unleashing the Potential of Precision Agriculture, pp. 401–407, Bologna, Italy, 2/7/2023- 6/7/2023
DOI: 10.3920/978-90-8686-947-3_49
Metrics:
Dainelli R., Martinelli M., Bruno A., Moroni D., Morelli S., Silvestri M., Ferrari E., Rocchi L., Toscano P.
In this study, an automatic system based on open AI architectures was developed and fed with an in-house built image dataset to recognize seven of the most widespread and hard-to-control weeds in wheat in the Mediterranean environment. A total of 10810 images were collected from the post-emergence (S1 dataset) to the pre-flowering stage (S2 dataset). A selection of pictures available from online sources (S3, 825 images) was used as a final and further independent test of the proposed recognition tool. The AI tool in the ensemble configuration achieved 100% accuracy on the validation and test set both for S1 and S2, while for S3 an accuracy of approximately 70% was achieved for weed species in the post-emergence stage.Source: ECPA 2023 - The 14th European Conference on Precision Agriculture - Unleashing the Potential of Precision Agriculture, pp. 401–407, Bologna, Italy, 2/7/2023- 6/7/2023
DOI: 10.3920/978-90-8686-947-3_49
Metrics:
See at:
doi.org | www.wageningenacademic.com | CNR ExploRA
2023
Report
Unknown
Artificial intelligence for image classification of wheat's phytopathologies, pests and infestants
Bruno A., Martinelli M., Moroni D., Rocchi L., Morelli S., Ferrari E., Toscano P., Dainelli R.
In this work we present a solution to deal with issues related to wheat cultures: diseases, abiotic damages, weeds and pests. The core of this work is the EfficientNet architecture, in particular the b0 version which is the one with best accuracy/complexity ratio in our opinion. Results show that in this way accuracies are improved by +2% up to +10% while the complexity is unchanged. The models presented and tested during this work have been deployed and can be tested by using the mobile app Granoscan.Source: ISTI Working papers, 2023
Bruno A., Martinelli M., Moroni D., Rocchi L., Morelli S., Ferrari E., Toscano P., Dainelli R.
In this work we present a solution to deal with issues related to wheat cultures: diseases, abiotic damages, weeds and pests. The core of this work is the EfficientNet architecture, in particular the b0 version which is the one with best accuracy/complexity ratio in our opinion. Results show that in this way accuracies are improved by +2% up to +10% while the complexity is unchanged. The models presented and tested during this work have been deployed and can be tested by using the mobile app Granoscan.Source: ISTI Working papers, 2023
See at: CNR ExploRA
2023
Conference article
Open Access
Medical waste sorting: a computer vision approach for assisted primary sorting
Bruno A., Caudai C., Leone G. R., Martinelli M., Moroni D., Crotti F.
Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management requires special care and high costs. However, this kind of waste contains a large fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a computer vision approach for assisting in the primary sorting of med- ical waste. The feasibility of our approach is demonstrated on representative datasets we collected and made available to the community.Source: IWCIM2023 - 11th International Workshop on Computational Intelligence for Multimedia Understanding, Rhodes Island, Greece, 05/06/2023
DOI: 10.1109/icasspw59220.2023.10193520
Metrics:
Bruno A., Caudai C., Leone G. R., Martinelli M., Moroni D., Crotti F.
Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management requires special care and high costs. However, this kind of waste contains a large fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a computer vision approach for assisting in the primary sorting of med- ical waste. The feasibility of our approach is demonstrated on representative datasets we collected and made available to the community.Source: IWCIM2023 - 11th International Workshop on Computational Intelligence for Multimedia Understanding, Rhodes Island, Greece, 05/06/2023
DOI: 10.1109/icasspw59220.2023.10193520
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Journal article
Open Access
Efficient lung ultrasound classification
Bruno A., Ignesti G., Salvetti O., Moroni D., Martinelli M.
A machine learning method for classifying Lung UltraSound is here proposed to provide a point of care tool for supporting a safe, fast and accurate diagnosis, that can also be useful during a pandemic like as SARS-CoV-2. Given the advantages (e.g. safety, rapidity, portability, cost-effectiveness) provided by the ultrasound technology over other methods (e.g. X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest LUS public dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art. The complexity of this solution keeps the number of parameters in the same order as an EfficientNet-b0 by adopting specific design choices that are adaptive ensembling with a combination layer, ensembling performed on the deep features, minimal ensemble only two weak models. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where the focus is on an inaccurate weak model versus an accurate model.Source: Bioengineering (Basel) 10 (2023). doi:10.3390/bioengineering10050555
DOI: 10.3390/bioengineering10050555
Metrics:
Bruno A., Ignesti G., Salvetti O., Moroni D., Martinelli M.
A machine learning method for classifying Lung UltraSound is here proposed to provide a point of care tool for supporting a safe, fast and accurate diagnosis, that can also be useful during a pandemic like as SARS-CoV-2. Given the advantages (e.g. safety, rapidity, portability, cost-effectiveness) provided by the ultrasound technology over other methods (e.g. X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest LUS public dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art. The complexity of this solution keeps the number of parameters in the same order as an EfficientNet-b0 by adopting specific design choices that are adaptive ensembling with a combination layer, ensembling performed on the deep features, minimal ensemble only two weak models. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where the focus is on an inaccurate weak model versus an accurate model.Source: Bioengineering (Basel) 10 (2023). doi:10.3390/bioengineering10050555
DOI: 10.3390/bioengineering10050555
Metrics:
See at:
ISTI Repository | www.mdpi.com | CNR ExploRA
2023
Conference article
Open Access
Efficient deep learning approach for olive disease classification
Bruno A., Moroni D., Martinelli M.
From ancient times olive tree cultivation has been one of the most crucial agricultural activities for Mediterranean countries. In recent years, the role of Artificial Intelligence in agriculture is increasing: its use ranges from monitoring of cultivated soil, to irrigation management, to yield prediction, to autonomous agricultural robots, to weed and pest classification and management, for example, by taking pictures using a standard smartphone or an unmanned aerial vehicle , and all this eases human work and makes it even more accessible. In this work, a method is proposed for olive disease classification, based on an adaptive ensemble of two EfficientNet-b0 models, that improves the state-of-the-art accuracy on a publicly available dataset by 1.6-2.6%. Both in terms of the number of parameters and the number of operations, our method reduces complexity roughly by 50% and 80%, respectively, that is a level not seen in at least a decade. Due to its efficiency, this method is also embeddable into a smartphone application for real-time processing.Source: ACSIS 2023 - 18th Conference on Computer Science and Intelligence Systems, pp. 889–894, Warsaw, Poland, 17-20/9/2023
DOI: 10.15439/2023f4794
Metrics:
Bruno A., Moroni D., Martinelli M.
From ancient times olive tree cultivation has been one of the most crucial agricultural activities for Mediterranean countries. In recent years, the role of Artificial Intelligence in agriculture is increasing: its use ranges from monitoring of cultivated soil, to irrigation management, to yield prediction, to autonomous agricultural robots, to weed and pest classification and management, for example, by taking pictures using a standard smartphone or an unmanned aerial vehicle , and all this eases human work and makes it even more accessible. In this work, a method is proposed for olive disease classification, based on an adaptive ensemble of two EfficientNet-b0 models, that improves the state-of-the-art accuracy on a publicly available dataset by 1.6-2.6%. Both in terms of the number of parameters and the number of operations, our method reduces complexity roughly by 50% and 80%, respectively, that is a level not seen in at least a decade. Due to its efficiency, this method is also embeddable into a smartphone application for real-time processing.Source: ACSIS 2023 - 18th Conference on Computer Science and Intelligence Systems, pp. 889–894, Warsaw, Poland, 17-20/9/2023
DOI: 10.15439/2023f4794
Metrics:
See at:
ISTI Repository | annals-csis.org | CNR ExploRA
2023
Report
Unknown
Artificial Intelligence in TiAssisto: first results
Ignesti G., Bruno A., Deri C., D'Angelo G., Salvetti O., Moroni D., Pratali L., Martinelli M.
Artificial Intelligence (AI) is integrated into medical applications since its beginning. The advent of deep learning algorithms, powerful computation power and large datasets has made possible the development of numerous new medical applications. A significant part of these applications is focused on the classification or on the segmentation of medical images. In this paper we present an innovative solution for clinical images classification.Source: ISTI Working papers, 2023
Ignesti G., Bruno A., Deri C., D'Angelo G., Salvetti O., Moroni D., Pratali L., Martinelli M.
Artificial Intelligence (AI) is integrated into medical applications since its beginning. The advent of deep learning algorithms, powerful computation power and large datasets has made possible the development of numerous new medical applications. A significant part of these applications is focused on the classification or on the segmentation of medical images. In this paper we present an innovative solution for clinical images classification.Source: ISTI Working papers, 2023
See at: CNR ExploRA
2023
Contribution to book
Open Access
Improving plant disease classification by adaptive minimal ensembling
Bruno A., Moroni D., Dainelli R., Rocchi L., Toscano P., Morelli S., Ferrari E., Martinelli M.
A novel method for improving plant disease classification, a challenging and time-consuming process, is proposed. First, using as baseline EfficientNet, a recent and advanced family of architectures having an excellent accuracy/complexity trade-off, we have introduced, devised, and applied refined techniques based on transfer learning, regularization, stratification, weighted metrics, and advanced optimizers in order to achieve improved performance. Then, we go further by introducing adaptive minimal ensembling, which is a unique input to the knowledge base of the proposed solution. This represents a leap forward since it allows improving the accuracy with limited complexity using only two EfficientNet-b0 weak models, performing ensembling on feature vectors by a trainable layer instead of classic aggregation on outputs. To the best of our knowledge, such an approach to ensembling has never been used before in literature. Our method was tested on PlantVillage, a public reference dataset used for benchmarking models' performances for crop disease diagnostic, considering both its original and augmented versions. We noticeably improved the state of the art by achieving 100% accuracy in both the original and augmented datasets. Results were obtained using PyTorch to train, test, and validate the models; reproducibility is granted by providing exhaustive details, including hyperparameters used in the experimentation. A Web interface is also made publicly available to test the proposed methods.Source: Computer vision in plant phenotyping and agriculture, edited by Valerio Giuffrida, Hanno Scharr, Ian Stavness, pp. 250–263. Lausanne: Frontiers media SA, 2023
Bruno A., Moroni D., Dainelli R., Rocchi L., Toscano P., Morelli S., Ferrari E., Martinelli M.
A novel method for improving plant disease classification, a challenging and time-consuming process, is proposed. First, using as baseline EfficientNet, a recent and advanced family of architectures having an excellent accuracy/complexity trade-off, we have introduced, devised, and applied refined techniques based on transfer learning, regularization, stratification, weighted metrics, and advanced optimizers in order to achieve improved performance. Then, we go further by introducing adaptive minimal ensembling, which is a unique input to the knowledge base of the proposed solution. This represents a leap forward since it allows improving the accuracy with limited complexity using only two EfficientNet-b0 weak models, performing ensembling on feature vectors by a trainable layer instead of classic aggregation on outputs. To the best of our knowledge, such an approach to ensembling has never been used before in literature. Our method was tested on PlantVillage, a public reference dataset used for benchmarking models' performances for crop disease diagnostic, considering both its original and augmented versions. We noticeably improved the state of the art by achieving 100% accuracy in both the original and augmented datasets. Results were obtained using PyTorch to train, test, and validate the models; reproducibility is granted by providing exhaustive details, including hyperparameters used in the experimentation. A Web interface is also made publicly available to test the proposed methods.Source: Computer vision in plant phenotyping and agriculture, edited by Valerio Giuffrida, Hanno Scharr, Ian Stavness, pp. 250–263. Lausanne: Frontiers media SA, 2023
See at:
ISTI Repository | www.frontiersin.org | CNR ExploRA
2023
Journal article
Open Access
Explaining ensemble models for lung ultrasound classification
Bruno A., Ignesti G, Martinelli M.
Correct classification is the main aspect in evaluating the quality of an artificial intelligence system, but what happens when you reach top accuracy and no method explains how it works? In our study, we aim at addressing the black-box problem using an ad-hoc built classifier for lung ultrasound images.Source: ERCIM news 134 (2023).
Bruno A., Ignesti G, Martinelli M.
Correct classification is the main aspect in evaluating the quality of an artificial intelligence system, but what happens when you reach top accuracy and no method explains how it works? In our study, we aim at addressing the black-box problem using an ad-hoc built classifier for lung ultrasound images.Source: ERCIM news 134 (2023).
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Deep learning methods for point-of-care ultrasound examination
Ignesti G., Deri C., D'Angelo G., Pratali L., Bruno A., Benassi A., Salvetti O., Moroni D., Martinelli M.
Point-of-care Test (POCT) is the delivery of medical care at or near the patient's bedside. Primarily employed in emergencies, where rapid diagnosis and treatment are critical, POCT is now being used in domestic telehealth solutions, as in the TiAssisto project, thanks to technological advances such as the development of portable and affordable devices, high-speed Internet connections, video conferencing, and Artificial Intelligence (AI). Ultrasound (US) images of internal organs and structures are valuable tools in POCT medicine since this examination is portable, quick, and cost-effective. USs can help diagnose different conditions, including heart problems, abdominal pain, and pneumonia. Deep learning algorithms have proven to be highly effective in image recognition, enabling physicians to make informed decisions on-site. This article presents and investigates a decision support system based on deep learning algorithms. The primary aim of this research is to detect various signs in US images using cutting-edge classification methods. The proposed pipeline initially adopts an optical character recognition (OCR) algorithm: this technique inspects and cleans the US image, ensuring privacy and better classification potential. The collected images are forwarded to a state-of-the-art (SOTA) deep learning network, a fine-tuned EfficientNET-b0, able to detect any signs potentially related to pathology in a rapid way. The network classification is then assessed in the pipeline using a visual explanation method, i.e. Grad-CAM, to evaluate if the proper medical signs were identified, offering a quick and effective second opinion. The involved physician's feedback remarks that this system can detect important signs in pulmonary US imaging, although the dataset is not yet the final one since the TiAssisto project is still ongoing, with a planned conclusion in February 2024. Our ultimate goal is not merely to develop a classification system but to create an effective healthcare support system that can be used beyond primary healthcare facilities.Source: SITIS 2023 - 17th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 436–441, Bangkok, Thailand, 8-10/11/2023
DOI: 10.1109/sitis61268.2023.00078
Metrics:
Ignesti G., Deri C., D'Angelo G., Pratali L., Bruno A., Benassi A., Salvetti O., Moroni D., Martinelli M.
Point-of-care Test (POCT) is the delivery of medical care at or near the patient's bedside. Primarily employed in emergencies, where rapid diagnosis and treatment are critical, POCT is now being used in domestic telehealth solutions, as in the TiAssisto project, thanks to technological advances such as the development of portable and affordable devices, high-speed Internet connections, video conferencing, and Artificial Intelligence (AI). Ultrasound (US) images of internal organs and structures are valuable tools in POCT medicine since this examination is portable, quick, and cost-effective. USs can help diagnose different conditions, including heart problems, abdominal pain, and pneumonia. Deep learning algorithms have proven to be highly effective in image recognition, enabling physicians to make informed decisions on-site. This article presents and investigates a decision support system based on deep learning algorithms. The primary aim of this research is to detect various signs in US images using cutting-edge classification methods. The proposed pipeline initially adopts an optical character recognition (OCR) algorithm: this technique inspects and cleans the US image, ensuring privacy and better classification potential. The collected images are forwarded to a state-of-the-art (SOTA) deep learning network, a fine-tuned EfficientNET-b0, able to detect any signs potentially related to pathology in a rapid way. The network classification is then assessed in the pipeline using a visual explanation method, i.e. Grad-CAM, to evaluate if the proper medical signs were identified, offering a quick and effective second opinion. The involved physician's feedback remarks that this system can detect important signs in pulmonary US imaging, although the dataset is not yet the final one since the TiAssisto project is still ongoing, with a planned conclusion in February 2024. Our ultimate goal is not merely to develop a classification system but to create an effective healthcare support system that can be used beyond primary healthcare facilities.Source: SITIS 2023 - 17th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 436–441, Bangkok, Thailand, 8-10/11/2023
DOI: 10.1109/sitis61268.2023.00078
Metrics:
See at:
ISTI Repository | CNR ExploRA
2023
Contribution to conference
Restricted
Efficient lung ultrasound classification
Ignesti G., Bruno A., Martinelli M., Moroni D.
The SARS-CoV-2 pandemic has taught us that point-of-care signs quickly or in remote settings are essential. Ultrasound imaging is a fast and common diagnostic tool, which made it a popular choice during the pandemic. Our team implemented a deep learning algorithm with remarkable accuracy (100%) to detect signs of COVID-19 and bacterial pneumonia, which can better assist physicians. GradCAM was employed to examine the outcomes and determine whether the network relied on dependable medical indicators for classification.Source: VISMAC 2023 - International Summer School on Machine Vision, Padova, Italy, 04-08/09/2023
Ignesti G., Bruno A., Martinelli M., Moroni D.
The SARS-CoV-2 pandemic has taught us that point-of-care signs quickly or in remote settings are essential. Ultrasound imaging is a fast and common diagnostic tool, which made it a popular choice during the pandemic. Our team implemented a deep learning algorithm with remarkable accuracy (100%) to detect signs of COVID-19 and bacterial pneumonia, which can better assist physicians. GradCAM was employed to examine the outcomes and determine whether the network relied on dependable medical indicators for classification.Source: VISMAC 2023 - International Summer School on Machine Vision, Padova, Italy, 04-08/09/2023
See at:
vismac23.github.io | CNR ExploRA
2023
Contribution to conference
Open Access
Trustworthy AI for signals and image processing: a telemedicine perspective
Ignesti G., Bruno A., Moroni D., Martinelli M.
Artficial Intelligence is showing unprecedented performance in signals & image processing. Classification, segmentation and generative process seem to have unlimited potential. The roots of Artificial Intelligence are deep in scientific history, but in the world of Big Data and Internet 5.0, its use and effects have yet to be entirely tested. The black box problem, security, privacy issues, and public opinion are some of the factors that push towards the development of a new concept: "Trustworthy AI". The use of advanced methods, such as EfficientNet & GradCAM, leads to remarkable accuracy and consistent explanation in the classification of ultrasound. Further studies aim at analyzing results could lead to a more robust application of AI in the generalized field of signal and image processing and will lay the foundation for future work on reliable AI.Source: AI & Society 2023 Summer School, La Maddalena, Italy, 05-09/06/2023
Ignesti G., Bruno A., Moroni D., Martinelli M.
Artficial Intelligence is showing unprecedented performance in signals & image processing. Classification, segmentation and generative process seem to have unlimited potential. The roots of Artificial Intelligence are deep in scientific history, but in the world of Big Data and Internet 5.0, its use and effects have yet to be entirely tested. The black box problem, security, privacy issues, and public opinion are some of the factors that push towards the development of a new concept: "Trustworthy AI". The use of advanced methods, such as EfficientNet & GradCAM, leads to remarkable accuracy and consistent explanation in the classification of ultrasound. Further studies aim at analyzing results could lead to a more robust application of AI in the generalized field of signal and image processing and will lay the foundation for future work on reliable AI.Source: AI & Society 2023 Summer School, La Maddalena, Italy, 05-09/06/2023
See at:
ISTI Repository | sites.google.com | CNR ExploRA
2022
Journal article
Open Access
Improving plant disease classification by adaptive minimal ensembling
Bruno A., Moroni D., Dainelli R., Rocchi L., Toscano P., Morelli S., Ferrari E., Martinelli M.
A novel method for improving plant disease classification, a challenging and time-consuming process, is proposed. First, using as baseline EfficientNet, a recent and advanced family of architectures having an excellent accuracy/complexity trade-off, we have introduced, devised, and applied refined techniques based on transfer learning, regularization, stratification, weighted metrics, and advanced optimizers in order to achieve improved performance. Then, we go further by introducing adaptive minimal ensembling, which is a unique input to the knowledge base of the proposed solution. This represents a leap forward since it allows improving the accuracy with limited complexity using only two EfficientNet-b0 weak models, performing ensembling on feature vectors by a trainable layer instead of classic aggregation on outputs. To the best of our knowledge, such an approach to ensembling has never been used before in literature. Our method was tested on PlantVillage, a public reference dataset used for benchmarking models' performances for crop disease diagnostic, considering both its original and augmented versions. We noticeably improved the state of the art by achieving 100% accuracy in both the original and augmented datasets. Results were obtained using PyTorch to train, test, and validate the models; reproducibility is granted by providing exhaustive details, including hyperparameters used in the experimentation. A Web interface is also made publicly available to test the proposed methods.Source: Frontiers in artificial intelligence (2022). doi:10.3389/frai.2022.868926
DOI: 10.3389/frai.2022.868926
Metrics:
Bruno A., Moroni D., Dainelli R., Rocchi L., Toscano P., Morelli S., Ferrari E., Martinelli M.
A novel method for improving plant disease classification, a challenging and time-consuming process, is proposed. First, using as baseline EfficientNet, a recent and advanced family of architectures having an excellent accuracy/complexity trade-off, we have introduced, devised, and applied refined techniques based on transfer learning, regularization, stratification, weighted metrics, and advanced optimizers in order to achieve improved performance. Then, we go further by introducing adaptive minimal ensembling, which is a unique input to the knowledge base of the proposed solution. This represents a leap forward since it allows improving the accuracy with limited complexity using only two EfficientNet-b0 weak models, performing ensembling on feature vectors by a trainable layer instead of classic aggregation on outputs. To the best of our knowledge, such an approach to ensembling has never been used before in literature. Our method was tested on PlantVillage, a public reference dataset used for benchmarking models' performances for crop disease diagnostic, considering both its original and augmented versions. We noticeably improved the state of the art by achieving 100% accuracy in both the original and augmented datasets. Results were obtained using PyTorch to train, test, and validate the models; reproducibility is granted by providing exhaustive details, including hyperparameters used in the experimentation. A Web interface is also made publicly available to test the proposed methods.Source: Frontiers in artificial intelligence (2022). doi:10.3389/frai.2022.868926
DOI: 10.3389/frai.2022.868926
Metrics:
See at:
ISTI Repository | ISTI Repository | www.frontiersin.org | CNR ExploRA
2022
Report
Unknown
Barilla Agrosat+ Aggiornamento 1/22
Bruno A., Moroni D., Martinelli M.
Nuovi modelli, miglioramenti, to-do list, progetto Barilla Agrosat+Source: ISTI Project report, Barilla Agrosat+, 2022
Bruno A., Moroni D., Martinelli M.
Nuovi modelli, miglioramenti, to-do list, progetto Barilla Agrosat+Source: ISTI Project report, Barilla Agrosat+, 2022
See at: CNR ExploRA
2022
Report
Unknown
Barilla Agrosat+ Aggiornamento 2/22
Bruno A., Moroni D., Martinelli M.
Nuovi modelli, miglioramenti, to-do list, progetto Barilla Agrosat+.Source: ISTI Project report, Barilla Agrosat+, 2022
Bruno A., Moroni D., Martinelli M.
Nuovi modelli, miglioramenti, to-do list, progetto Barilla Agrosat+.Source: ISTI Project report, Barilla Agrosat+, 2022
See at: CNR ExploRA
2022
Conference article
Open Access
Augmented reality, artificial intelligence and machine learning in Industry 4.0: case studies at SI-Lab
Bruno A, Coscetti S, Leone G. R., Germanese D., Magrini M., Martinelli M., Moroni D., Pascali M. A., Pieri G., Reggiannini M., Tampucci M.
In recent years, the impressive advances in artificial intelligence, computer vision, pervasive computing, and augmented reality made them rise to pillars of the fourth industrial revolution. This short paper aims to provide a brief survey of current use cases in factory applications and industrial inspection under active development at the Signals and Images Lab, ISTI-CNR, Pisa.Source: Ital-IA 2022 - Convegno nazionale CINI sull'Intelligenza Artificiale, Torino, Italy, 9-11/02/2022
DOI: 10.5281/zenodo.6322733
Metrics:
Bruno A, Coscetti S, Leone G. R., Germanese D., Magrini M., Martinelli M., Moroni D., Pascali M. A., Pieri G., Reggiannini M., Tampucci M.
In recent years, the impressive advances in artificial intelligence, computer vision, pervasive computing, and augmented reality made them rise to pillars of the fourth industrial revolution. This short paper aims to provide a brief survey of current use cases in factory applications and industrial inspection under active development at the Signals and Images Lab, ISTI-CNR, Pisa.Source: Ital-IA 2022 - Convegno nazionale CINI sull'Intelligenza Artificiale, Torino, Italy, 9-11/02/2022
DOI: 10.5281/zenodo.6322733
Metrics:
See at:
ISTI Repository | www.ital-ia2022.it | CNR ExploRA
2022
Report
Unknown
Barilla Agrosat+ : aggiornamento 4/22
Bruno A., Moroni D., Martinelli M.
Nuovi modelli, miglioramenti, riepilogo, progetto Barilla Agrosat+Source: ISTI Project report, Barilla Agrosat+, 2022
Bruno A., Moroni D., Martinelli M.
Nuovi modelli, miglioramenti, riepilogo, progetto Barilla Agrosat+Source: ISTI Project report, Barilla Agrosat+, 2022
See at: CNR ExploRA
2022
Report
Unknown
Barilla Agrosat+ : aggiornamento 3/22
Bruno A., Moroni D., Martinelli M.
Nuovo modello, miglioramenti, prossimi step progetto Barilla Agrosat+.Source: ISTI Project report, Barilla Agrosat+, 2022
Bruno A., Moroni D., Martinelli M.
Nuovo modello, miglioramenti, prossimi step progetto Barilla Agrosat+.Source: ISTI Project report, Barilla Agrosat+, 2022
See at: CNR ExploRA