2 citations · 5 across the 5 of their papers we have counts for
5 papers
ProtoSSL: Self-Supervised Pretraining and Downstream Transfer for Projection-Based Prototype Models
Steven Song, Sahil Sethi, Brett Beaulieu-Jones +1
In domains where both predictive performance and interpretability are essential, deep neural networks achieve strong results but provide limited insight into how their predictions…
Prototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs
Sahil Sethi, David Chen, Michael C. Burkhart +3
Prototype-based neural networks offer interpretable predictions by comparing inputs to learned, representative signal patterns anchored in training data. While such models have sho…
SCOPE-MRI: Bankart Lesion Detection as a Case Study in Data Curation and Deep Learning for Challenging Diagnoses
Sahil Sethi, Sai Reddy, Mansi Sakarvadia +4
Deep learning has shown strong performance in musculoskeletal imaging, but prior work has largely targeted conditions where diagnosis is relatively straightforward. More challengin…
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
Sahil Sethi, David Chen, Thomas Statchen +4
Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanatio…
Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning
Sahil Sethi, Sai Reddy, Mansi Sakarvadia +4
Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI ar…