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eess.IV2023★ 58 cited
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides
Pedro C. Neto, Diana Montezuma, Sara P. Oliveira +10
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-sl…
eess.IV2020
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
Christian Abbet, Inti Zlobec, Behzad Bozorgtabar +1
With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is us…