85 citations · 565 across the 57 of their papers we have counts for
24 papers · 1 filter
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…
Evaluating Contextual Intelligence in Recyclability: A Comprehensive Study of Image-Based Reasoning Systems
Eliot Park, Abhi Kumar, Pranav Rajpurkar
While the importance of efficient recycling is widely acknowledged, accurately determining the recyclability of items and their proper disposal remains a complex task for the gener…
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…
ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priors
Romain Hardy, Tyler Berzin, Pranav Rajpurkar
Three-dimensional (3D) scene understanding in colonoscopy presents significant challenges that necessitate automated methods for accurate depth estimation. However, existing depth…
RadGame: An AI-Powered Platform for Radiology Education
Mohammed Baharoon, Siavash Raissi, John S. Jun +29
We introduce RadGame, an AI-powered gamified platform for radiology education that targets two core skills: localizing findings and generating reports. Traditional radiology traini…
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…
MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting
Arnold Caleb Asiimwe, Dídac Surís, Pranav Rajpurkar +1
In medical reporting, the accuracy of radiological reports, whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper: image-condi…
RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models
Serena Zhang, Sraavya Sambara, Oishi Banerjee +3
Generating accurate radiology reports from medical images is a clinically important but challenging task. While current Vision Language Models (VLMs) show promise, they are prone t…
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…
MedVersa: A Generalist Foundation Model for Medical Image Interpretation
Hong-Yu Zhou, Julián Nicolás Acosta, Subathra Adithan +3
Current medical AI systems are often limited to narrow applications, hindering widespread adoption. We present MedVersa, a generalist foundation model trained on tens of millions o…
Multimodal Foundation Models Exploit Text to Make Medical Image Predictions
Thomas Buckley, James A. Diao, Pranav Rajpurkar +2
Multimodal foundation models have shown compelling but conflicting performance in medical image interpretation. However, the mechanisms by which these models integrate and prioriti…
Augmenting medical image classifiers with synthetic data from latent diffusion models
Luke W. Sagers, James A. Diao, Luke Melas-Kyriazi +6
While hundreds of artificial intelligence (AI) algorithms are now approved or cleared by the US Food and Drugs Administration (FDA), many studies have shown inconsistent generaliza…
Med-Flamingo: a Multimodal Medical Few-shot Learner
Michael Moor, Qian Huang, Shirley Wu +6
Medicine, by its nature, is a multifaceted domain that requires the synthesis of information across various modalities. Medical generative vision-language models (VLMs) make a firs…
BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors
Kathryn Wantlin, Chenwei Wu, Shih-Cheng Huang +12
Medical data poses a daunting challenge for AI algorithms: it exists in many different modalities, experiences frequent distribution shifts, and suffers from a scarcity of examples…
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning
Akshay Smit, Damir Vrabac, Yujie He +3
We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources. Our method…
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays
Emma Chen, Andy Kim, Rayan Krishnan +3
A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work,…
CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays
Siyu Shi, Ishaan Malhi, Kevin Tran +2
We systematically evaluate the performance of deep learning models in the presence of diseases not labeled for or present during training. First, we evaluate whether deep learning…
CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation
Alexander Ke, William Ellsworth, Oishi Banerjee +2
Deep learning methods for chest X-ray interpretation typically rely on pretrained models developed for ImageNet. This paradigm assumes that better ImageNet architectures perform be…
CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation
Soham Gadgil, Mark Endo, Emily Wen +2
Medical image segmentation models are typically supervised by expert annotations at the pixel-level, which can be expensive to acquire. In this work, we propose a method that combi…
MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models
Hari Sowrirajan, Jingbo Yang, Andrew Y. Ng +1
Contrastive learning is a form of self-supervision that can leverage unlabeled data to produce pretrained models. While contrastive learning has demonstrated promising results on n…
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set
Damir Vrabac, Akshay Smit, Rebecca Rojansky +5
Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistentl…