3 citations · 5 across the 6 of their papers we have counts for
8 papers
LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling
Abhishek Moturu, Babak Taati, Anna Goldenberg
Label noise is common in medical imaging datasets due to factors such as inter-rater variability, annotation errors, and ambiguous cases. This can severely undermine the reliabilit…
Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory
Matt Ratto, Abhishek Moturu, Daniel Silver
As AI systems are deployed across increasingly diverse social contexts, alignment can no longer be framed as the optimization of a single, unified set of values. Instead, systems m…
LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection
Abhishek Moturu, Anna Goldenberg, Babak Taati
Synthetic data is useful only when the added samples fill missing parts of the training distribution that matter for the downstream task. We introduce LiBaGS, a lightweight, genera…
When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains
Ahmadreza Jeddi, Kimia Shaban, Negin Baghbanzadeh +4
Reinforcement learning (RL) is increasingly used to post-train medical Vision-Language Models (VLMs), yet it remains unclear whether RL improves medical visual reasoning or mainly…
SynPAIN: A Synthetic Dataset of Pain and Non-Pain Facial Expressions
Babak Taati, Muhammad Muzammil, Yasamin Zarghami +5
Accurate pain assessment in patients with limited ability to communicate, such as older adults with severe dementia, represents a critical healthcare challenge. Robust automated sy…
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI
Alex Chang, Vinith Suriyakumar, Abhishek Moturu +5
Modern deep unsupervised learning methods have shown great promise for detecting diseases across a variety of medical imaging modalities. While previous generative modeling approac…