2 citations · 6 across the 16 of their papers we have counts for
17 papers
ProTok: Unified Protein Tokenization via Modular Multi-Codebook Quantization
Romain Hardy, Xiaoman Zhang, Pranav Rajpurkar
Protein sequence, structure, and functional annotations each capture complementary aspects of protein biology, yet existing tokenizers encode each modality independently, limiting…
Imitation Learning for Robot Assistance in Open Surgery: A Multi-Policy Evaluation on Suture Following
Xucheng Wang, Zhizhou Yang, Xiaoman Zhang +3
This study presents the first evaluation of general-purpose imitation learning for surgeon-robot collaborative assistance in open surgery, targeting suture following: the grab-pull…
A systematic evaluation of vision-language models for observational astronomical reasoning tasks
Wenke Ren, Hengxiao Guo, Wenwen Zuo +1
Vision-language models (VLMs) are increasingly proposed as general-purpose tools for scientific data interpretation, yet their reliability on real astronomical observations across…
ReXSonoVQA: A Video QA Benchmark for Procedure-Centric Ultrasound Understanding
Xucheng Wang, Xiaoman Zhang, Sung Eun Kim +2
Ultrasound acquisition requires skilled probe manipulation and real-time adjustments. Vision-language models (VLMs) could enable autonomous ultrasound systems, but existing benchma…
ReXInTheWild: A Unified Benchmark for Medical Photograph Understanding
Oishi Banerjee, Sung Eun Kim, Alexandra N. Willauer +9
Everyday photographs taken with ordinary cameras are already widely used in telemedicine and other online health conversations, yet no comprehensive benchmark evaluates whether vis…
Do Mixed-Vendor Multi-Agent LLMs Improve Clinical Diagnosis?
Grace Chang Yuan, Xiaoman Zhang, Sung Eun Kim +1
Multi-agent large language model (LLM) systems have emerged as a promising approach for clinical diagnosis, leveraging collaboration among agents to refine medical reasoning. Howev…
ReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challenges
Roshan Kenia, Xiaoman Zhang, Pranav Rajpurkar
Autonomous coding agents built on large language models (LLMs) can now solve many general software and machine learning tasks, but they remain ineffective on complex, domain-specif…
3DReasonKnee: Advancing Grounded Reasoning in Medical Vision Language Models
Sraavya Sambara, Sung Eun Kim, Xiaoman Zhang +5
Current Vision-Language Models (VLMs) struggle to ground anatomical regions in 3D medical images and reason about them in a step-by-step manner, a key requirement of real-world dia…
ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
Mohammed Baharoon, Luyang Luo, Michael Moritz +22
We introduce ReXGroundingCT, the first publicly available dataset linking free-text findings to pixel-level 3D segmentations in chest CT scans. The dataset includes 3,142 non-contr…
Voice-guided Orchestrated Intelligence for Clinical Evaluation (VOICE): A Voice AI Agent System for Prehospital Stroke Assessment
Julian Acosta, Scott Adams, Julius Kernbach +7
We developed a voice-driven artificial intelligence (AI) system that guides anyone - from paramedics to family members - through expert-level stroke evaluations using natural conve…
ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding
Ankit Pal, Jung-Oh Lee, Xiaoman Zhang +5
We present ReXVQA, the largest and most comprehensive benchmark for visual question answering (VQA) in chest radiology, comprising approximately 696,000 questions paired with 160,0…
ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports
Xiaoman Zhang, Julián N. Acosta, Josh Miller +2
We present ReXGradient-160K, representing the largest publicly available chest X-ray dataset to date in terms of the number of patients. This dataset contains 160,000 chest X-ray s…
FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models
Alice Heiman, Xiaoman Zhang, Emma Chen +2
Medical vision-language models often struggle with generating accurate quantitative measurements in radiology reports, leading to hallucinations that undermine clinical reliability…
ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation
Xiaoman Zhang, Hong-Yu Zhou, Xiaoli Yang +6
AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively eva…
ReXplain: Translating Radiology into Patient-Friendly Video Reports
Luyang Luo, Jenanan Vairavamurthy, Xiaoman Zhang +7
Radiology reports, designed for efficient communication between medical experts, often remain incomprehensible to patients. This inaccessibility could potentially lead to anxiety,…
HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation
Julián N. Acosta, Xiaoman Zhang, Siddhant Dogra +5
We present Head CT Ontology Normalized Evaluation (HeadCT-ONE), a metric for evaluating head CT report generation through ontology-normalized entity and relation extraction. HeadCT…
Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs
Xiaoman Zhang, Julián N. Acosta, Hong-Yu Zhou +1
Recent advancements in artificial intelligence have significantly improved the automatic generation of radiology reports. However, existing evaluation methods fail to reveal the mo…