Chest X-Ray

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12 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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Concept-Guided Noisy Negative Suppression for Zero-Shot Classification and Grounding of Chest X-Ray Findings
May 19, 2026Chenyu Lian, Hong-Yu Zhou, Chun-Ka Wong +1Chest X-Ray Adaptive Boundary Suppression
The Center for Smart Health, School of Nursing, the Hong Kong Polytechnic University, Hong Kong, China · Research Institute for Smart Ageing, the Hong Kong Polytechnic University,2026 Hong Kong, China · School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing, China +1
Flow Matching with Optimized Subclass Priors for Medical Image Augmentation
May 15, 2026Felix Nützel, Mischa Dombrowski, Bernhard KainzData Augmentation Chest X-Ray
Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany · Department of Computing, Imperial College London, London SW7 2AZ, UK
MI-CXR: A Benchmark for Longitudinal Reasoning over Multi-Interval Chest X-rays
May 15, 2026Sunghwan Steve Cho, Yunseok Han, Jaeyoung DoChest X-Ray Clinical Reasoning Training
AIDAS Laboratory, 1ECE & 2IPAI, Seoul National University
Few-Shot Synthetic Data Generation with Diffusion Models for Downstream Vision Tasks
May 12, 2026Daniil Dushenev, Nazariy Karpov, Daniil Zinovjev +2Synthetic Data Medical Image Generation
National University of Science and Technology MISIS, Moscow, Russia
CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography
May 11, 2026Eva Prakash, Yunhe Gao, Chong Wang +10Chest X-Ray Spatial Supervision
Stanford University · University of Oxford · University of California, Berkeley +2
Radiologist-Guided Causal Concept Bottleneck Models for Chest X-Ray Interpretation
May 8, 2026Amy Rafferty, Rishi Ramaesh, Ajitha RajanChest X-Ray Dermoscopic Concept Bottleneck Models
University of Edinburgh, UK · NHS Lothian, UK
CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs
May 7, 2026Zhengru Fang, Yanan Ma, Yu Guo +5Chest X-Ray Medical Vision-Language Models
City University of Hong Kong · Tsinghua Shenzhen International Graduate School
Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones
May 4, 2026Duy Nguyen Huu, Duy Hoang Khuong, Ngu Huynh Cong VietChest X-Ray Medical Image Classification
Department of Artificial Intelligence FPT University Ho Chi Minh City, Vietnam · Department of Computing Fundamental FPT University Ho Chi Minh City, Vietnam
Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification
May 4, 2026Duy Hoang Khuong, Duy Nguyen Huu, Ngu Huynh Cong VietMedical Image Classification Chest X-Ray
Department of Artificial Intelligence FPT University Ho Chi Minh City, Vietnam · Department of Computing Fundamental FPT University Ho Chi Minh City, Vietnam
Are Data Augmentation and Segmentation Always Necessary? Insights from COVID-19 X-Rays and a Methodology Thereof
Apr 29, 2026Aman Swaraj, Arnav Agarwal, Hitendra Singh Bhadouria +2Chest X-Ray Data Augmentation
Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, India – 247667 · United Health Group, Optum division · Department of Computer Science and Engineering, National Institute of Technology, Delhi, India – 110036
CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation
Apr 29, 2026Sonali Sharma, Jin Long, George Shih +7Chest X-Ray Medical Vision-Language Models
Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, CA, USA · Department of Radiology, Stanford University, Stanford, CA, USA · Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA +6
When To Adapt? Adapting the Model or Data in Federated Medical Imaging
Apr 27, 2026Chamani Shiranthika, Parvaneh SaeediHeterogeneous Federated Learning Heterogeneity
School of Engineering Science, Simon Fraser University, Burnaby, BC, V5A 1S6
Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings
Apr 27, 2026Sebastian Cajas Ordóñez, Felipe Ocampo Osorio, Dax Enshan Koh +10Quantum Kernels Medical Image Classification
1MIT Critical Data, Massachusetts Institute of Technology, Cambridge, MA, USA · 2Clinical Research Center, Artificial Intelligence Unit, Fundaci´on Valle del Lili, Cali, Valle del Cauca, Colombia · 3Quantum Innovation Centre (Q.InC), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore +15
SparseContrast: Dynamic Sparse Attention for Efficient and Accurate Contrastive Learning in Medical Imaging
Apr 27, 2026Paarth Prasad, Ruchika MalhotraMedical Imaging Chest X-Ray
Department of Software Engineering, Delhi Technological University
CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
Apr 24, 2026Ashwin Kumar, Robbie Holland, Corey Barrett +8Chest X-Ray Medical Vision-Language Models
Stanford AIMI, Stanford University · Oracle Health AI · Department of Radiology, Stanford University
CXRMate-2: Structured Multimodal Temporal Embeddings and Tractable Reinforcement Learning for Clinically Acceptable Chest X-ray Radiology Report Generation
Apr 21, 2026Aaron Nicolson, Elizabeth J. Cooper, Hwan-Jin Yoon +7Chest X-Ray Radiology Report Generation
Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane & Melbourne, Australia · Princess Alexandra Hospital, Metro South Health, Brisbane, Australia · Queensland Digital Health Centre, University of Queensland, Brisbane, Australia +2
ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification
Apr 20, 2026Florian Kittler, Sheethal Bhat, Andreas MaierChest X-Ray Vision-Language Model Adaptation
Friedrich-Alexander University Erlangen-Nuremberg, Erlangen 91054, Germany
CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
Apr 16, 2026Hexin Dong, Yi Lin, Pengyu Zhou +25Chest X-Ray Medical Imaging Datasets
RadJEPA: Radiology Encoder for Chest X-Rays via Joint Embedding Predictive Architecture
Jan 22, 2026Anas Anwarul Haq Khan, Mariam Husain, Pratik Jalan +1Radiology Report Generation Chest X-Ray
Department of Computer Science (CSE), Indian Institute of Technology Bombay, India · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Department of Biomedical Engineering, Johns Hopkins University, USA +1
CXRAgent: Director-Orchestrated Multi-Stage Reasoning for Chest X-Ray Interpretation
Oct 24, 2025Jinhui Lou, Yan Yang, Zhou Yu +4Chest X-Ray Clinical Reasoning Training
School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China · Department of Automation, Tsinghua University, Beijing 100084, China · Department of Colorectal Medical Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, China +2
Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context
Sep 24, 2025Andrew Wang, Jiashuo Zhang, Michael OberstChest X-Ray Clinical
Brown University · Johns Hopkins University
Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models
Jun 12, 2025Konstantinos Vilouras, Ilias Stogiannidis, Junyu Yan +2Chest X-Ray Radiology Report Generation
School of Engineering, University of Edinburgh · Canon Medical Research Europe Ltd.

May 19, 2026, cs.CVJ/K move · Enter open · S save

Concept-Guided Noisy Negative Suppression for Zero-Shot Classification and Grounding of Chest X-Ray Findings

Chenyu Lian, Hong-Yu Zhou, Chun-Ka Wong, Jing Qin

The Center for Smart Health, School of Nursing, the Hong Kong Polytechnic University, Hong Kong, China · Research Institute for Smart Ageing, the Hong Kong Polytechnic University,2026 Hong Kong, China · School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing, China+1

Chest X-Ray Adaptive Boundary Suppression Suppression Grounding Evidential Uncertainty

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Vision-language alignment using chest X-rays and radiology reports has emerged as an advanced paradigm for zero-shot classification and grounding of chest X-ray findings. However, standard contrastive learning typically treats radiographs and reports from different patients simply as negative pairs. This assumption introduces noisy negatives, as different patients frequently exhibit similar findings. Such noisy negatives cause semantic ambiguity and degrade performance in zero-shot understanding tasks. To address this challenge, we propose CoNNS, a concept-guided noisy-negative suppression framework. To support the negative suppression mechanism, unlike previous methods that use raw reports or templatized texts, we construct a hierarchical concept ontology using large language models. The ontology structures 41 key clinical concepts by explicitly modeling presence, attributes (location and characteristics), and texts (evidential segment and presence statement). Leveraging this ontology, we implement a cross-patient pair relabeling strategy comprising three steps: (1) Fine-Grained Breakdown to categorize pairs based on finding presence; (2) Noisy Negative Filtering to resolve semantic conflicts by removing false negatives; and (3) Hard Negative Mining to identify subtle attribute discrepancies using a lightweight language model. Finally, we propose a Concept-Aware NCE loss to align visual features with text while suppressing the identified noisy negatives. Extensive experiments across multi-granularity zero-shot grounding tasks and five zero-shot classification datasets validate that CoNNS outperforms existing state-of-the-art models. The code is available at https://github.com/DopamineLcy/conns.

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