Document - On the effectiveness of 3D vision transformers for the prediction of prostate cancer aggressiveness

2022

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On the effectiveness of 3D vision transformers for the prediction of prostate cancer aggressiveness

Pachetti E., Colantonio S., Pascali M. A.

Vision Transformers Prostate cancer ProstateX-2

Prostate cancer is the most frequent male neoplasm in European men. To date, the gold standard for determining the aggressiveness of this tumor is the biopsy, an invasive and uncomfortable procedure. Before the biopsy, physicians recommend an investigation by multiparametric magnetic resonance imaging, which may serve the radiologist to gather an initial assessment of the tumor. The study presented in this work aims to investigate the role of Vision Transformers in predicting prostate cancer aggressiveness based only on imaging data. We designed a 3D Vision Transformer able to process volumetric scans, and we optimized it on the ProstateX-2 challenge dataset by training it from scratch. As a term of comparison, we also designed a 3D Convolutional Neural Network, and we optimized it in a similar fashion. The results obtained by our preliminary investigations show that Vision Transformers, even without extensive optimization and customization, can ensure an improved performance with respect to Convolutional Neural Networks and might be comparable with other more fine-tuned solutions.

Source: Image Analysis and Processing, edited by Mazzeo P.L., Frontoni E., Sclaroff S., Distante C., pp. 317–328. Switzerland: Springer International Publishing, 2022

Publisher: Springer International Publishing, Switzerland, CHE

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@inbook{oai:it.cnr:prodotti:469998,
	title = {On the effectiveness of 3D vision transformers for the prediction of prostate cancer aggressiveness},
	author = {Pachetti E. and Colantonio S. and Pascali M. A.},
	publisher = {Springer International Publishing, Switzerland, CHE},
	doi = {10.1007/978-3-031-13324-4_27},
	booktitle = {Image Analysis and Processing, edited by Mazzeo P.L., Frontoni E., Sclaroff S., Distante C., pp. 317–328. Switzerland: Springer International Publishing, 2022},
	year = {2022}
}