4 citations · 14 across the 9 of their papers we have counts for
5 papers · 1 filter
Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts
Andong Tan, Fengtao Zhou, Hao Chen
The concept bottleneck model (CBM) is an interpretable-by-design framework that makes decisions by first predicting a set of interpretable concepts, and then predicting the class l…
Multimodal Data Integration for Precision Oncology: Challenges and Future Directions
Huajun Zhou, Fengtao Zhou, Chenyu Zhao +3
The essence of precision oncology lies in its commitment to tailor targeted treatments and care measures to each patient based on the individual characteristics of the tumor. The i…
Post-hoc Part-prototype Networks
Andong Tan, Fengtao Zhou, Hao Chen
Post-hoc explainability methods such as Grad-CAM are popular because they do not influence the performance of a trained model. However, they mainly reveal "where" a model looks at…
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis
Huajun Zhou, Fengtao Zhou, Hao Chen
Recently, we have witnessed impressive achievements in cancer survival analysis by integrating multimodal data, e.g., pathology images and genomic profiles. However, the heterogene…
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer
Fengtao Zhou, Yingxue Xu, Yanfen Cui +11
Gastric cancer (GC) is a prevalent malignancy worldwide, ranking as the fifth most common cancer with over 1 million new cases and 700 thousand deaths in 2020. Locally advanced gas…