Chest X-Ray Classification

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5 papers in the last four weeks, down 37% on the four weeks before. 0.0% of all new papers.

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Leveraging Pathology Co-occurrence for Test-Time Adaptation in Chest X-Ray Diagnosis
Jul 4, 2026Woojin Jeong, Yujin Choi, Dongbin Kim +2Chest X-Ray Classification Test-Time Adaptation
Seoul National University, 1, Gwanak-ro, Seoul, 08826, Republic of Korea · Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore · UNIST, 50, UNIST-gil, Ulsan, 44919, Republic of Korea
TRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling
Jul 1, 2026Tong Shao, Hongshun Ling, Li Zhang +4Long-Tailed Classification Chest X-Ray Classification
School of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, China
Accurate Recognition of Pneumonia and COVID-19 by Geometric Shape Normalization of Lung Region using Automatic Landmark Detection and Piecewise Affine Warping
Jun 29, 2026Salvador E. Ayala-Raggi, Rafael Alejandro Cruz-Ovando, Lauro Reyes-Cocoletzi +1Chest X-Ray Classification Medical Imaging
Facultad de Ciencias de la Electrónica Benemérita Universidad Autónoma de Puebla Puebla, México
NoduLoCC2026: Lung Nodule Localization and Classification Contest from Chest X-Ray Images
Jun 19, 2026Adnan Mustafic, Halim Benhabiles, Adnane Cabani +19Chest X-Ray Classification Medical Image Anomaly Detection
Universit´e de Haute-Alsace, IRIMAS, 68100 Mulhouse, France
Translating Inference-Time Control to Radiology Vision-Language Models: Activation Steering for Pneumonia Classification on Chest X-rays
Jun 18, 2026Eduardo Moreno Judice de Mattos Farina, Mateus A. Esmeraldo, Felipe Akio Matsuoka +2Chest X-Ray Classification Radiology VLMs
Universidade Federal de São Paulo (UNIFESP) Hospital Israelita Albert Einstein São Paulo, Brazil · Department of Radiology Stanford University School of Medicine Stanford, CA, USA · Universidade Federal de São Paulo (UNIFESP) DASA São Paulo, Brazil +2
Frozen Foundation-Model Embeddings Discard Small-Lesion Signal in Chest Radiography: Implications for Pre-Deployment Evaluation
Jun 10, 2026Raajitha Muthyala, Zhenan Yin, Alekhya Jilla +6Chest X-Ray Classification Medical Imaging Foundation Models
Department of Biomedical Engineering and Informatics, Indiana University, 535 W Michigan St., IT 475J, Indianapolis, IN, 46202, USA · Department of Radiology and Imaging Sciences, Emory University, 1364 Clifton Rd NE, Atlanta, GA, 30322, USA
A unified multi-task framework enables interpretable chest radiograph analysis
Jun 2, 2026Lijian Xu, Ziyu Ni, Xinglong Liu +3Radiology Report Generation Chest X-Ray Classification
Shenzhen University of Advanced Technology · the Chinese University of Hong Kong, Hong Kong · Lead contact +4
PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder
Jun 1, 2026Yancheng Liu, Kenichi Maeda, Manan PancholyCross-Modal Knowledge Distillation Chest X-Ray Classification
Department of Computer Science, Brown University, Providence, RI, USA.
A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery
May 19, 2026Sarmad Khan, Arslan Shaukat, Umer Asgher +1Chest X-Ray Classification Medical Image Classification
Department of Computer & Software Engineering National University of Sciences & Technology Islamabad, Pakistan · School of Computing & Information Systems University of Melbourne Melbourne, Australia
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
CXRAgent: Director-Orchestrated Multi-Stage Reasoning for Chest X-Ray Interpretation
Oct 24, 2025Jinhui Lou, Yan Yang, Zhou Yu +4Chest X-Ray Classification LLM Agent Orchestration
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 Classification Distribution Shift
Brown University · Johns Hopkins University

Jul 4, 2026, cs.CVJ/K move · Enter open · S save

Leveraging Pathology Co-occurrence for Test-Time Adaptation in Chest X-Ray Diagnosis

Woojin Jeong, Yujin Choi, Dongbin Kim, Soyeon Park+1

Seoul National University, 1, Gwanak-ro, Seoul, 08826, Republic of Korea · Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore · UNIST, 50, UNIST-gil, Ulsan, 44919, Republic of Korea

Chest X-Ray Classification Test-Time Adaptation Medical Image Classification

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Medical imaging models often degrade when deployed at new clinical sites due to differences in imaging equipment, protocols, and patient populations. Test-time adaptation (TTA) addresses this by updating a pretrained model using only unlabeled target data, without access to source data. However, existing TTA methods were designed for single-label classification on natural image benchmarks, minimizing entropy uniformly across all samples without considering label dependencies. This overlooks a key property of multi-label medical imaging: pathologies do not occur independently but exhibit structured co-occurrence patterns. In this work, we propose Co-occurrence Weighted Adaptation (CoWA), which leverages disease co-occurrence patterns as a reliability signal for adaptation. CoWA estimates label co-occurrence structure from model predictions and downweights samples that deviate from expected patterns, enabling adaptation to rely more on consistent predictions while reducing the impact of noisy ones. We evaluate CoWA on chest X-ray benchmarks under domain shifts and demonstrate consistent improvements over established baselines.

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