20 citations · 55 across the 9 of their papers we have counts for
19 papers
Augmentation by Counterfactual Explanation -- Fixing an Overconfident Classifier
Sumedha Singla, Nihal Murali, Forough Arabshahi +2
A highly accurate but overconfident model is ill-suited for deployment in critical applications such as healthcare and autonomous driving. The classification outcome should reflect…
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation
Yanwu Xu, Shaoan Xie, Wenhao Wu +3
Unpaired image-to-image translation (I2I) is an ill-posed problem, as an infinite number of translation functions can map the source domain distribution to the target distribution.…
Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision
Yanwu Xu, Mingming Gong, Shaoan Xie +1
Deep learning has achieved remarkable success in medicalimage segmentation, but it usually requires a large numberof images labeled with fine-grained segmentation masks, andthe ann…
Using Causal Analysis for Conceptual Deep Learning Explanation
Sumedha Singla, Stephen Wallace, Sofia Triantafillou +1
Model explainability is essential for the creation of trustworthy Machine Learning models in healthcare. An ideal explanation resembles the decision-making process of a domain expe…
Self-Supervised Vessel Enhancement Using Flow-Based Consistencies
Rohit Jena, Sumedha Singla, Kayhan Batmanghelich
Vessel segmentation is an essential task in many clinical applications. Although supervised methods have achieved state-of-art performance, acquiring expert annotation is laborious…
Context Matters: Graph-based Self-supervised Representation Learning for Medical Images
Li Sun, Ke Yu, Kayhan Batmanghelich
Supervised learning method requires a large volume of annotated datasets. Collecting such datasets is time-consuming and expensive. Until now, very few annotated COVID-19 imaging d…