11 citations · 28 across the 17 of their papers we have counts for
4 papers · 1 filter
Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning
Shuvendu Roy, Yasaman Parhizkar, Franklin Ogidi +5
We perform a comprehensive benchmarking of contrastive frameworks for learning multimodal representations in the medical domain. Through this study, we aim to answer the following…
Consistency-Guided Asynchronous Contrastive Tuning for Few-Shot Class-Incremental Tuning of Foundation Models
Shuvendu Roy, Elham Dolatabadi, Arash Afkanpour +1
We propose Consistency-guided Asynchronous Contrastive Tuning (CoACT), a novel method for continuously tuning foundation models to learn new classes in few-shot settings. CoACT con…
EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records
Adibvafa Fallahpour, Mahshid Alinoori, Wenqian Ye +3
Transformers have significantly advanced the modeling of Electronic Health Records (EHR), yet their deployment in real-world healthcare is limited by several key challenges. Firstl…
Can Generative Models Improve Self-Supervised Representation Learning?
Sana Ayromlou, Vahid Reza Khazaie, Fereshteh Forghani +1
The rapid advancement in self-supervised representation learning has highlighted its potential to leverage unlabeled data for learning rich visual representations. However, the exi…