7 citations · 16 across the 5 of their papers we have counts for
5 papers
A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models
Gabriele Campanella, Shengjia Chen, Ruchika Verma +10
The use of self-supervised learning (SSL) to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantitie…
Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective
Shengjia Chen, Gabriele Campanella, Abdulkadir Elmas +8
Recent advances in artificial intelligence (AI), in particular self-supervised learning of foundation models (FMs), are revolutionizing medical imaging and computational pathology…
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…
Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms
Shengjia Chen, Nikunj Gupta, Woodward B. Galbraith +2
Rheumatoid arthritis (RA) is an autoimmune condition caused when patients' immune system mistakenly targets their own tissue. Machine learning (ML) has the potential to identify pa…