35 citations · 52 across the 10 of their papers we have counts for
10 papers
Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction
Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu +10
While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present R…
Foundation Models for Clinical Records at Health System Scale
Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu +5
Large-scale pretraining has transformed modeling of language and other data types, but its potential remains underexplored in healthcare with structured electronic health records (…
3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography
Weicheng Zhu, Haoxu Huang, Huanze Tang +10
Head computed tomography (CT) imaging is a widely-used imaging modality with multitudes of medical indications, particularly in assessing pathology of the brain, skull, and cerebro…
A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction
Qidong Yang, Weicheng Zhu, Joseph Keslin +3
Probabilistic prediction of sequences from images and other high-dimensional data is a key challenge, particularly in risk-sensitive applications. In these settings, it is often de…
Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling
Weicheng Zhu, Sheng Liu, Carlos Fernandez-Granda +1
Self-supervised learning (SSL) has emerged as a powerful technique for learning rich representations from unlabeled data. The data representations are able to capture many underlyi…
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
Kangning Liu, Weicheng Zhu, Yiqiu Shen +4
Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown p…
Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning
Weicheng Zhu, Carlos Fernandez-Granda, Narges Razavian
Lung squamous cell carcinoma (LSCC) has a high recurrence and metastasis rate. Factors influencing recurrence and metastasis are currently unknown and there are no distinct histopa…
Deep Probability Estimation
Sheng Liu, Aakash Kaku, Weicheng Zhu +8
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained…
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
Sheng Liu, Kangning Liu, Weicheng Zhu +2
Deep learning in the presence of noisy annotations has been studied extensively in classification, but much less in segmentation tasks. In this work, we study the learning dynamics…
Variationally Regularized Graph-based Representation Learning for Electronic Health Records
Weicheng Zhu, Narges Razavian
Electronic Health Records (EHR) are high-dimensional data with implicit connections among thousands of medical concepts. These connections, for instance, the co-occurrence of disea…