4 citations · 11 across the 14 of their papers we have counts for
17 papers
MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models
Aofei Chang, Le Huang, Alex James Boyd +4
Medical large vision-language models (Med-LVLMs) have recently achieved remarkable progress in vision-language comprehension and medical image segmentation. However, existing model…
Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes
Alex Boyd, Andrew Warrington, Taha Kass-Hout +2
Foundational marked temporal point process (MTPP) models, such as the Hawkes process, often use inexpressive model families in order to offer interpretable parameterizations of eve…
Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization
Chenliang Li, Adel Elmahdy, Alex Boyd +7
Reinforcement learning (RL) algorithms such as PPO and GRPO are widely used to train large language models (LLMs) for multi-turn agentic tasks. However, in off-policy training pipe…
MammoDINO: Anatomically Aware Self-Supervision for Mammographic Images
Sicheng Zhou, Lei Wu, Cao Xiao +2
Self-supervised learning (SSL) has transformed vision encoder training in general domains but remains underutilized in medical imaging due to limited data and domain specific biase…
Decipher-MR: A Vision-Language Foundation Model for 3D MRI Representations
Zhijian Yang, Noel DSouza, Istvan Megyeri +11
Magnetic Resonance Imaging is a critical imaging modality in clinical diagnosis and research, yet its complexity and heterogeneity hinder scalable, generalizable machine learning.…
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning
Aofei Chang, Le Huang, Alex James Boyd +4
Medical Large Vision-Language Models (Med-LVLMs) often exhibit suboptimal attention distribution on visual inputs, leading to hallucinated or inaccurate outputs. Existing mitigatio…
Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector
Haoyan Yang, Runxue Bao, Cao Xiao +4
LLM-as-a-Judge has emerged as a promising tool for automatically evaluating generated outputs, but its reliability is often undermined by potential biases in judgment. Existing eff…
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation
Aishik Konwer, Zhijian Yang, Erhan Bas +4
Foundational models such as the Segment Anything Model (SAM) are gaining traction in medical imaging segmentation, supporting multiple downstream tasks. However, such models are su…
MedHEval: Benchmarking Hallucinations and Mitigation Strategies in Medical Large Vision-Language Models
Aofei Chang, Le Huang, Parminder Bhatia +3
Large Vision Language Models (LVLMs) are becoming increasingly important in the medical domain, yet Medical LVLMs (Med-LVLMs) frequently generate hallucinations due to limited expe…
Deep Continuous-Time State-Space Models for Marked Event Sequences
Yuxin Chang, Alex Boyd, Cao Xiao +4
Marked temporal point processes (MTPPs) model sequences of events occurring at irregular time intervals, with wide-ranging applications in fields such as healthcare, finance and so…
BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models
Aofei Chang, Jiaqi Wang, Han Liu +4
Parameter Efficient Fine-Tuning (PEFT) offers an efficient solution for fine-tuning large pretrained language models for downstream tasks. However, most PEFT strategies are manuall…
Dynamic Uncertainty Ranking: Enhancing Retrieval-Augmented In-Context Learning for Long-Tail Knowledge in LLMs
Shuyang Yu, Runxue Bao, Parminder Bhatia +3
Large language models (LLMs) can learn vast amounts of knowledge from diverse domains during pre-training. However, long-tail knowledge from specialized domains is often scarce and…
Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images
Longchao Da, Rui Wang, Xiaojian Xu +4
Medical imaging is crucial for diagnosing a patient's health condition, and accurate segmentation of these images is essential for isolating regions of interest to ensure precise d…
Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval
Pengcheng Jiang, Cao Xiao, Minhao Jiang +4
Large language models (LLMs) have demonstrated significant potential in clinical decision support. Yet LLMs still suffer from hallucinations and lack fine-grained contextual medica…
KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
Pengcheng Jiang, Lang Cao, Cao Xiao +3
Knowledge Graph Embedding (KGE) techniques are crucial in learning compact representations of entities and relations within a knowledge graph, facilitating efficient reasoning and…
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
Xiyao Wang, Jiuhai Chen, Zhaoyang Wang +8
Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. Howev…
SonoSAMTrack -- Segment and Track Anything on Ultrasound Images
Hariharan Ravishankar, Rohan Patil, Vikram Melapudi +5
In this paper, we present SonoSAMTrack - that combines a promptable foundational model for segmenting objects of interest on ultrasound images called SonoSAM, with a state-of-the a…