92 citations · 333 across the 16 of their papers we have counts for
21 papers
Which Modality Decides? Counterfactual Modality Attribution for Multimodal LLMs
Vahidin Hasic, Chao Wang, Luis C. Garcia-Peraza-Herrera +2
Multimodal large language models (MLLMs) increasingly support high-stakes decision making by combining complementary information from images and text. While existing explainability…
Can LLM-Generated Text Empower Surgical Vision-Language Pre-training?
Chengan Che, Chao Wang, Jiayuan Huang +2
Recent advancements in self-supervised learning have led to powerful surgical vision encoders capable of spatiotemporal understanding. However, extending these visual foundations t…
Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations
Chao Wang, Chengan Che, Xinyue Chen +2
Counterfactual explanations (CFEs) are minimal and semantically meaningful modifications of the input of a model that alter the model predictions. They highlight the decisive featu…
A Stitch in Time: Learning Procedural Workflow via Self-Supervised Plackett-Luce Ranking
Chengan Che, Chao Wang, Xinyue Chen +2
Procedural activities, ranging from routine cooking to complex surgical operations, are highly structured sequences of actions performed in a specific temporal order. Despite the s…
Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation
Theodore Barfoot, Luis C. Garcia-Peraza-Herrera, Samet Akcay +2
Deep neural networks for medical image segmentation are often overconfident, compromising both reliability and clinical utility. In this work, we propose differentiable formulation…
LEMON: A Large Endoscopic MONocular Dataset and Foundation Model for Perception in Surgical Settings
Chengan Che, Chao Wang, Tom Vercauteren +2
Traditional open-access datasets focusing on surgical procedures are often limited by their small size, typically consisting of fewer than 100 videos and less than 30 hours of foot…