Search Results for author:
Found 6 papers, 3 papers with code
A CAD System for Colorectal Cancer from WSI: A Clinically Validated Interpretable ML-based Prototype
On the largest of those sets, the proposed approach achieved an accuracy of 93. 44%.
Panoptic segmentation with highly imbalanced semantic labels
We describe here the panoptic segmentation method we devised for our participation in the CoNIC: Colon Nuclei Identification and Counting Challenge at ISBI 2022.
Towards IID representation learning and its application on biomedical data
Compared to the SOTA baselines supported in WILDS, the results confirm the superior performance of IID representation learning on OOD tasks.
Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection
In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets.
Visible and Invisible: Causal Variable Learning and its Application in a Cancer Study
Causal visual discovery is a fundamental yet challenging problem in many research fields.
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer.
