58 citations · 59 across the 4 of their papers we have counts for
4 papers
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…
Towards IID representation learning and its application on biomedical data
Jiqing Wu, Inti Zlobec, Maxime Lafarge +2
Due to the heterogeneity of real-world data, the widely accepted independent and identically distributed (IID) assumption has been criticized in recent studies on causality. In thi…
Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection
Christian Abbet, Linda Studer, Andreas Fischer +4
Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology. Making use of open-source data…
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…