62 citations · 94 across the 15 of their papers we have counts for
19 papers
A4-MR: Abnormality-aware Anatomical Alignment for MR
Noel D'souza, Zhijian Yang, Nishank Singla +14
Recent advances in large-scale foundation models have enabled strong generalization across a wide range of clinical and research tasks in magnetic resonance imaging (MRI). However,…
Next-Dense-Stride Prediction for Multimodal Autoregressive Visual Modeling
Chicago Y. Park, Jialin Mao, Xiaojian Xu +3
We introduce DenseAR, a new generative paradigm that reformulates autoregressive image generation as coarse-to-fine next-dense-stride prediction using a compact single-scale tokeni…
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…
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…
One-shot Localization and Segmentation of Medical Images with Foundation Models
Deepa Anand, Gurunath Reddy M, Vanika Singhal +10
Recent advances in Vision Transformers (ViT) and Stable Diffusion (SD) models with their ability to capture rich semantic features of the image have been used for image corresponde…
Improving Early Sepsis Prediction with Multi Modal Learning
Fred Qin, Vivek Madan, Ujjwal Ratan +4
Sepsis is a life-threatening disease with high morbidity, mortality and healthcare costs. The early prediction and administration of antibiotics and intravenous fluids is considere…
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature
Parminder Bhatia, Lan Liu, Kristjan Arumae +12
Coronavirus disease (COVID-19) has been declared as a pandemic by WHO with thousands of cases being reported each day. Numerous scientific articles are being published on the disea…
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Mengqi Jin, Mohammad Taha Bahadori, Aaron Colak +11
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinic…