{"id":755,"date":"2021-04-17T21:51:05","date_gmt":"2021-04-17T21:51:05","guid":{"rendered":"https:\/\/aequip.it\/?post_type=articoli&#038;p=755"},"modified":"2021-04-18T14:54:37","modified_gmt":"2021-04-18T14:54:37","slug":"a-hybrid-deep-learning-approach-for-gland-segmentation-in-prostate-histopathological-images","status":"publish","type":"articoli","link":"https:\/\/aequip.it\/en\/articoli\/a-hybrid-deep-learning-approach-for-gland-segmentation-in-prostate-histopathological-images\/","title":{"rendered":"A hybrid deep learning approach for gland segmentation in prostate histopathological images"},"excerpt":{"rendered":"<p>In digital pathology, the morphology and architecture of prostate glands have been routinely adopted by pathologists to evaluate the presence of cancer tissue. The manual annotations are operator-dependent, error-prone and time-consuming. 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