activity
20192022
most citedMedCLIP: Contrastive Learning from Unpaired Medical Images and Text

31 citations · 52 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CV202231 cited

MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Zifeng Wang, Zhenbang Wu, Dinesh Agarwal +1

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

cs.CL202211 cited

PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning

Zifeng Wang, Jimeng Sun

Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) is challenging due to privacy concerns, which hinders the use of ML for healthcare applications. Synthetic EH…

q-bio.QM202210 cited

Artificial Intelligence for In Silico Clinical Trials: A Review

Zifeng Wang, Chufan Gao, Lucas M. Glass +1

A clinical trial is an essential step in drug development, which is often costly and time-consuming. In silico trials are clinical trials conducted digitally through simulation and…

cs.AI2021

Lifelong Learning based Disease Diagnosis on Clinical Notes

Zifeng Wang, Yifan Yang, Rui Wen +3

Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model on new tasks usually lea…

cs.LG2020

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback

Zifeng Wang, Xi Chen, Rui Wen +3

Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommen…

cs.LG2020

Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks

Zifeng Wang, Rui Wen, Xi Chen +4

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two main chall…