Pneumonia

Mask-Guided Attention Regulation for Anatomically Consistent Counterfactual CXR Synthesis

Counterfactual generation for chest X-rays (CXR) aims to simulate plausible pathological changes while preserving patient-specific anatomy. However, diffusion-based editing methods often suffer from structural drift, where stable anatomical semantics propagate globally through attention and distort non-target regions, and unstable pathology expression, since subtle and localized lesions induce wea...

Learning functional groups in complex microbiomes

From soil to the gut, communities composed of thousands of microbes perform functions such as carbon sequestration and immune system regulation. Here, we introduce a data-driven approach that explains how community function can be traced to just a few groups of microbes or genes. In gut communities, our neural-network based clustering algorithm correctly recovers known functional groups. In the oc...

Loss Design and Architecture Selection for Long-Tailed Multi-Label Chest X-Ray Classification

Long-tailed class distributions pose a significant challenge for multi-label chest X-ray (CXR) classification, where rare but clinically important fin...

Leveraging large language models to address common vaccination myths and misconceptions

Large language models (LLMs) are increasingly used by the public to seek health information, yet their reliability in addressing common vaccine myths ...

t2pmhc: A Structure-Informed Graph Neural Network to predict TCR-pMHC Binding

Mapping of T cell receptors (TCRs) to their cognate MHC-presented peptides (pMHC) is central for the development of precision immunotherapies and vacc...

When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusion

Machine learning holds promise for advancing clinical decision support, yet it remains unclear when multimodal learning truly helps in practice, parti...

Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering

Automated radiology report generation using vision-language models (VLMs) is limited by the risk of prior-comparison hallucination, where the model ge...

A multimodal slice discovery framework for systematic failure detection and explanation in medical image classification

Despite advances in machine learning-based medical image classifiers, the safety and reliability of these systems remain major concerns in practical s...

A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling

Foundation models for medical imaging are typically pretrained on increasingly large datasets, following a "scale-at-all-costs" paradigm. However, thi...

Can Agents Distinguish Visually Hard-to-Separate Diseases in a Zero-Shot Setting? A Pilot Study

The rapid progress of multimodal large language models (MLLMs) has led to increasing interest in agent-based systems. While most prior work in medical...

What Topological and Geometric Structure Do Biological Foundation Models Learn? Evidence from 141 Hypotheses

When biological foundation models such as scGPT and Geneformer process single-cell gene expression, what geometric and topological structure forms in ...

Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis

Computer-aided diagnosis (CAD) has significantly advanced automated chest X-ray diagnosis but remains isolated from clinical workflows and lacks relia...

Overview of the CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification

Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existi...

PSF-Med: Measuring and Explaining Paraphrase Sensitivity in Medical Vision Language Models

Medical Vision Language Models (VLMs) can change their answers when clinicians rephrase the same question, which raises deployment risks. We introduce...

Causal Decoding for Hallucination-Resistant Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet remain susceptible to object hallucination (introduc...

Automated Model Discovery Based on COVID-19 Epidemiologic Data

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative appr...

LUMEN: Longitudinal Multi-Modal Radiology Model for Prognosis and Diagnosis

Large vision-language models (VLMs) have evolved from general-purpose applications to specialized use cases such as in the clinical domain, demonstrat...

Genomic Evolution of SARS-CoV-2 Delta Variants Pre- and Post-Omicron Emergence using Alignment-free Machine Learning models

The SARS-CoV-2 Delta variant (B.1.617.2), initially classified as a variant of concern due to its enhanced transmissibility and vaccine-escape mutatio...

Personalized Longitudinal Medical Report Generation via Temporally-Aware Federated Adaptation

Longitudinal medical report generation is clinically important yet remains challenging due to strict privacy constraints and the evolving nature of di...

Obscuration to Clarity: Bone Suppression for Enhanced Localization in Pneumothorax Segmentation of Chest Radiographs

Chest radiography (CXR) is a primary modality for assessing cardiopulmonary conditions, but its effectiveness is limited by anatomical obstructions (e...