3 citations · 3 across the 5 of their papers we have counts for
5 papers
Self-Supervised Learning for Knee Osteoarthritis: Diagnostic Limitations and Prognostic Value of Hospital Data
Haresh Rengaraj Rajamohan, Yuxuan Chen, Kyunghyun Cho +1
This study assesses whether self-supervised learning (SSL) improves knee osteoarthritis (OA) modeling for diagnosis and prognosis relative to ImageNet-pretrained initialization. We…
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 (…
A Progressive Risk Formulation for Enhanced Deep Learning based Total Knee Replacement Prediction in Knee Osteoarthritis
Haresh Rengaraj Rajamohan, Richard Kijowski, Kyunghyun Cho +1
We developed deep learning models for predicting Total Knee Replacement (TKR) need within various time horizons in knee osteoarthritis patients, with a novel capability: the models…
MR-Transformer: Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging
Chaojie Zhang, Shengjia Chen, Ozkan Cigdem +4
A transformer-based deep learning model, MR-Transformer, was developed for total knee replacement (TKR) prediction using magnetic resonance imaging (MRI). The model incorporates th…
Estimation of Time-to-Total Knee Replacement Surgery
Ozkan Cigdem, Shengjia Chen, Chaojie Zhang +3
A survival analysis model for predicting time-to-total knee replacement (TKR) was developed using features from medical images and clinical measurements. Supervised and self-superv…