UR Computer Vision and Machine Learning week

Añade, copiando o insertando de manera manual, toda la información necesaria.

Prostate cancer stands as the most prevalent malignancy among men and ranks third in terms of mortality, exhibiting an escalating incidence trend. When detected at an early stage, the prognosis is favorable. However, an overestimation of the tumor's aggressiveness often leads to aggressive treatment options such as radical prostatectomy, which may entail substantial side effects, including urinary incontinence and erectile dysfunction. To accurately assess the disease's stage and aggressiveness, biopsies are conducted in patients presenting abnormal biomolecular indicators. This assessment is now standardized through the Gleason score. Nevertheless, it is semiquantitative and bears a significant degree of subjectivity tied to the experience of the uropathologist. In this context, we have employed a database of histologies meticulously annotated by a team of pathologists who analyzed specimens from radical prostatectomies. We have trained a classifier based on the Cross-Covariance Image Transformers architecture to identify distinct histological structures. The network's inference is complemented by a filtering process to characterize these pathological structures at the histological preparation level.

The trained classifier exhibits a 97% accuracy when evaluated in a test data-set. Preliminary findings in newly acquired images, wherein diverse patterns have been reconstructed, are promising, particularly in well-represented categories. However, it's essential to emphasize that these results are preliminary and warrant validation. The proposed methodology, when applied to biopsy images, offers a more flexible and less subjective means of identifying various patterns compared to the Gleason score. This approach not only enables a richer quantification but also paves the way for a more precise diagnostic process.