Search Results for author: Zhenbang Wu

Found 5 papers, 2 papers with code

CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models

1 code implementation10 Jun 2024 Peng Xia, Ze Chen, Juanxi Tian, Yangrui Gong, Ruibo Hou, Yue Xu, Zhenbang Wu, Zhiyuan Fan, Yiyang Zhou, Kangyu Zhu, Wenhao Zheng, Zhaoyang Wang, Xiao Wang, Xuchao Zhang, Chetan Bansal, Marc Niethammer, Junzhou Huang, Hongtu Zhu, Yun Li, Jimeng Sun, ZongYuan Ge, Gang Li, James Zou, Huaxiu Yao

Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare.

Fairness

MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

1 code implementation18 Oct 2022 Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, Jimeng Sun

Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction.

Contrastive Learning Image-text Retrieval +1

Knowledge-Driven New Drug Recommendation

no code implementations11 Oct 2022 Zhenbang Wu, Huaxiu Yao, Zhe Su, David M Liebovitz, Lucas M Glass, James Zou, Chelsea Finn, Jimeng Sun

However, newly approved drugs do not have much historical prescription data and cannot leverage existing drug recommendation methods.

Few-Shot Learning Multi-Label Classification

AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment

no code implementations4 Mar 2022 Zhenbang Wu, Cao Xiao, Lucas M Glass, David M Liebovitz, Jimeng Sun

To tackle this problem, we propose AutoMap to automatically map the medical codes across different EHR systems in a coarse-to-fine manner: (1) Ontology-level Alignment: We leverage the ontology structure to learn a coarse alignment between the source and target medical coding systems; (2) Code-level Refinement: We refine the alignment at a fine-grained code level for the downstream tasks using a teacher-student framework.

Mortality Prediction

Learning to Transfer via Modelling Multi-level Task Dependency

no code implementations25 Sep 2019 Haonan Wang, Zhenbang Wu, Ziniu Hu, Yizhou Sun

Besides, the understanding of relationships among tasks has been ignored by most of the current methods.

Multi-Task Learning

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