Chest X-Ray Classification

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Chest X-ray classification asks models to identify one or more abnormalities, such as pneumonia, effusion, or nodules, from radiographs, often to support screening and triage. Common approaches include CNNs and vision transformers trained on labeled images, report-supervised or contrastive vision-language pretraining, and methods that use anatomical regions, disease hierarchies, or co-occurrence; weak supervision and long-tailed learning address scarce or imbalanced findings. Models are evaluated on datasets such as NIH ChestX-ray14, CheXpert, MIMIC-CXR, PadChest, and VinDr-CXR, increasingly with tests of external-hospital transfer, rare-label recognition, zero-shot performance, localization, calibration, and subgroup outcomes. Strong benchmark scores do not guarantee reliable clinical performance: results can shift with institutions, populations, label definitions, and thresholds, while shortcuts and noisy report-derived labels complicate whether predictions reflect image evidence.

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100 papers10 papers in the last four weeks, down 41% on the four weeks before. 0.1% of all new papers.

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 +1Contrastive Learning Chest X-Ray Classification
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
Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis
May 18, 2026Danu KimChest X-Ray Classification Medical Diagnosis
Korea International School, Jeju Campus, Jeju-do 63644, Republic of Korea
CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography
May 11, 2026Eva Prakash, Yunhe Gao, Chong Wang +10VLM Robustness Chest X-Ray Classification
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 Classification 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 +5VLM Evaluation Chest X-Ray Classification
City University of Hong Kong · Tsinghua Shenzhen International Graduate School
A Robust Unsupervised Domain Adaptation Framework for Medical Image Classification Using RKHS-MMD
May 5, 2026Sapna Sachan, Rakesh Kumar Sanodiya, Amulya Kumar MahtoChest X-Ray Classification Unsupervised Domain Adaptation
Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, India · Indian Institute of Information Technology, Design and Manufacturing,2026 Jabalpur, India
Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones
May 4, 2026Duy Nguyen Huu, Duy Hoang Khuong, Ngu Huynh Cong VietClass-Imbalanced Learning Chest X-Ray 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 VietClass-Imbalanced Learning Long-Tailed Classification
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 Classification 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 Classification Medical Image Analysis
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
Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings
Apr 27, 2026Sebastian Cajas Ordóñez, Felipe Ocampo Osorio, Dax Enshan Koh +10Chest X-Ray Classification Healthcare
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 MalhotraContrastive Learning Chest X-Ray Classification
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 +8Radiology Report Generation Chest X-Ray Classification
Stanford AIMI, Stanford University · Oracle Health AI · Department of Radiology, Stanford University
ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification
Apr 20, 2026Florian Kittler, Sheethal Bhat, Andreas MaierVLM Robustness Chest X-Ray Classification
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 +25Zero-Shot Learning Long-Tailed Classification
Department of Population Health Sciences, Weill Cornell Medicine, New York, USA · Department of Radiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China · Department of Radiology, West China School of Medicine, Sichuan University, Sichuan University Affiliated Chengdu Second People’s Hospital, Chengdu Second People’s Hospital, Chengdu, China +20
CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability
Apr 14, 2026Utsho Kumar DeyChest X-Ray Classification Convolutional Neural Networks
Department of Computer Science and Engineering, Northern University of Business and Technology Khulna, Bangladesh
GazeVaLM: A Multi-Observer Eye-Tracking Benchmark for Evaluating Clinical Realism in AI-Generated X-Rays
Apr 13, 2026David Wong, Zeynep Isik, Bin Wang +22Medical Image Benchmarks VLM Evaluation
Northwestern University, Evanston, IL, United States · Northwestern University, Evanston, IL, USA · Université Sorbonne Paris Nord, Paris, France +5
When Fine-Tuning Changes the Evidence: Architecture-Dependent Semantic Drift in Chest X-Ray Explanations
Apr 9, 2026Kabilan Elangovan, Daniel TingExplainable Medical Image Analysis Transfer Learning
Singapore Health Services, Singapore · Singapore Eye Research Institute, Singapore
Temporal Inversion for Learning Interval Change in Chest X-Rays
Apr 6, 2026Hanbin Ko, Kyungmin Jeon, Doowoong Choi +1Longitudinal Medical Image Analysis Medical VLMs
Interdisciplinary Program in Bioengineering, Seoul National University Graduate School · Integrated Major in Innovative Medical Science, Seoul National University Graduate School · Dept. of Radiology, Seoul National University Hospital +2
XrayClaw: Cooperative-Competitive Multi-Agent Alignment for Trustworthy Chest X-ray Diagnosis
Apr 3, 2026Shawn Young, Lijian XuChest X-Ray Classification Multi-Agent Reasoning
Shenzhen University of Advanced Technology, Shenzhen, China
CXR-LT 2026 Challenge: Projection-Aware Multi-Label and Zero-Shot Chest X-Ray Classification
Apr 2, 2026Juno Cho, Dohui Kim, Mingeon Kim +3Chest X-Ray Classification Radiology VLMs
KAIST · KAIST, Daejeon, South Korea · GIST +5
A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation
Apr 1, 2026Yabin Zhang, Chong Wang, Yunhe Gao +19Radiology VLMs VLM Reasoning
Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, CA, USA. · Department of Radiology, Stanford University, Stanford, CA, USA. · Department of Computer Science, Stanford University, Stanford, CA, USA. +4
Feature-level Site Leakage Reduction for Cross-Hospital Chest X-ray Transfer via Self-Supervised Learning
Mar 31, 2026Ayoub Louaye Bouaziz, Lokmane CheboubaCross-Domain Transfer Learning Cross-Dataset Generalization
University of Mentouri Brothers Constantine 1, Constantine, Algeria · University of Paris-VIII-Vincennes-Saint-Denis, France · LRIA Laboratory, University of Science and Technology Houari Boumediene (USTHB), Bab-Ezzouar, Algeria +1
JDCNet: Confidence-Gated Privileged-Modality Distillation for Cost-Preserving X-ray Inference
Mar 31, 2026Bo Ma, Jinsong Wu, Weiqi Yan +2Cross-Modal Knowledge Distillation Knowledge Distillation
J. Wu is with the Guilin University of Electronic Technology, Guilin, China. · H. Wei is with Hikvision Technology Co., Ltd, Hangzhou, China. · K. Liu is with the Hebei University of Technology, Tianjin 300401, China.
Seeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays
Mar 27, 2026Kang Liu, Zhuoqi Ma, Siyu Liang +5Radiology VLMs Medical VLMs
Xidian University, Xi’an, China · Wuhan University, Wuhan, China
FixationFormer: Direct Utilization of Expert Gaze Trajectories for Chest X-Ray Classification
Mar 24, 2026Daniel Beckmann, Benjamin RisseChest X-Ray Classification Medical Image Classification
Institute for Geoinformatics, Department of Computer Science, University of Münster, Münster, Germany
WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classification
Mar 19, 2026Isabel Rio-Torto, Jaime S. Cardoso, Luís F. TeixeiraNatural Language Explanation Generation Explainable Medical Image Analysis
INESC TEC · Universidade do Porto
Clinically Aware Synthetic Image Generation for Concept Coverage in Chest X-ray Models
Mar 16, 2026Amy Rafferty, Rishi Ramaesh, Ajitha RajanSynthetic Data Augmentation Medical Image Synthesis
University of Edinburgh, UK · NHS Lothian, UK
Comparative Analysis of Deep Learning Architectures for Multi-Disease Classification of Single-Label Chest X-rays
Mar 11, 2026Ali M. Bahram, Saman Muhammad Omer, Hardi M. MohammedChest X-Ray Classification Medical Image Classification
Department of Computer Science, College of Science, Charmo University, 46023 Chamchamal, Kurdistan Region, Iraq · Department of Software Engineering, University of Raparin, Ranya, Kurdistan Region, Iraq
Efficient Chest X-ray Representation Learning via Semantic-Partitioned Contrastive Learning
Mar 7, 2026Wangyu Feng, Shawn Young, Lijian XuMedical Image Representation Learning Self-Supervised Pre-Training
Shenzhen University of Advanced Technology, Shenzhen, China

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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

Contrastive Learning Chest X-Ray Classification Medical VLMs Medical Image Grounding

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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.