3 citations · 7 across the 9 of their papers we have counts for
9 papers
MedVision: Benchmarking Quantitative Medical Image Analysis
Yongcheng Yao, Yongshuo Zong, Raman Dutt +3
Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., "Is this normal or abnormal?") or qualitative descriptive tasks.…
How to make Medical AI Systems safer? Simulating Vulnerabilities, and Threats in Multimodal Medical RAG System
Kaiwen Zuo, Zelin Liu, Raman Dutt +4
Large Vision-Language Models (LVLMs) augmented with Retrieval-Augmented Generation (RAG) are increasingly employed in medical AI to enhance factual grounding through external clini…
CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs
Raman Dutt, Pedro Sanchez, Yongchen Yao +3
Structured benchmarks have advanced text-conditional image generation for real-world imagery, however, no such benchmark exists for synthetic radiograph generation. Despite being a…
Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilities
Raman Dutt, Harleen Hanspal, Guoxuan Xia +5
In this work, we undertake the challenge of augmenting the existing generative capabilities of pre-trained text-only large language models (LLMs) with multi-modal generation capabi…
The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray Generation
Raman Dutt
Generative models, particularly text-to-image (T2I) diffusion models, play a crucial role in medical image analysis. However, these models are prone to training data memorization,…
Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion Models
Raman Dutt, Pedro Sanchez, Ondrej Bohdal +2
In this work, we present compelling evidence that controlling model capacity during fine-tuning can effectively mitigate memorization in diffusion models. Specifically, we demonstr…
BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning
Yuyang Xue, Junyu Yan, Raman Dutt +4
Developing models with robust group fairness properties is paramount, particularly in ethically sensitive domains such as medical diagnosis. Recent approaches to achieving fairness…
MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection
Raman Dutt, Ondrej Bohdal, Pedro Sanchez +2
Diffusion models excel in generating images that closely resemble their training data but are also susceptible to data memorization, raising privacy, ethical, and legal concerns, p…
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis
Raman Dutt, Ondrej Bohdal, Sotirios A. Tsaftaris +1
Training models with robust group fairness properties is crucial in ethically sensitive application areas such as medical diagnosis. Despite the growing body of work aiming to mini…