mlsanityData to 7 Oct 2026

EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learning

arXiv 2405.05237 ↗First posted 8 May 2024cs.CVcited by 22

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Abstract (arXiv)

The diagnosis and treatment of chest diseases play a crucial role in maintaining human health. X-ray examination has become the most common clinical examination means due to its efficiency and cost-effectiveness. Artificial intelligence analysis methods for chest X-ray images are limited by insufficient annotation data and varying levels of annotation, resulting in weak generalization ability and difficulty in clinical dissemination. Here we present EVA-X, an innovative foundational model based on X-ray images with broad applicability to various chest disease detection tasks. EVA-X is the first X-ray image based self-supervised learning method capable of capturing both semantic and geometric information from unlabeled images for universal X-ray image representation. Through extensive experimentation, EVA-X has demonstrated exceptional performance in chest disease analysis and localization, becoming the first model capable of spanning over 20 different chest diseases and achieving leading results in over 11 different detection tasks in the medical field. Additionally, EVA-X significantly reduces the burden of data annotation in the medical AI field, showcasing strong potential in the domain of few-shot learning. The emergence of EVA-X will greatly propel the development and application of foundational medical models, bringing about revolutionary changes in future medical research and clinical practice. Our codes and models are available at: https://github.com/hustvl/EVA-X.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 48 of its 71 references that are in the map. First posted on arXiv 8 May 2024.

Builds on

48 of 71 references.

References · 48

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36 more
  1. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 10; 2026: 11.

22 papers.

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  1. 2024EchoFM: Foundation Model for Generalizable Echocardiogram Analysiscited by 14
  2. 20253D Foundation Model for Generalizable Disease Detection in Head Computed Tomographycited by 10
  3. 2025M-SpecGene: Generalized Foundation Model for RGBT Multispectral Visioncited by 8
  4. 2025Towards All-in-One Medical Image Re-Identificationcited by 6
  5. 2025Chest X-ray Foundation Model with Global and Local Representations Integrationcited by 5
  6. 2025DeepSparse: A Foundation Model for Sparse-View CBCT Reconstructioncited by 4
  7. 2026UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generationcited by 3
  8. 2025Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutionscited by 2
  9. 2025CX-Mind: A Pioneering Multimodal Large Language Model for Interleaved Reasoning in Chest X-ray via Curriculum-Guided Reinforcement Learningcited by 2
  10. 2025A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practicecited by 2
  11. 2026The pretraining domain outweighs the training objective in setting the privacy-utility trade-off of differentially private medical image analysiscited by 2
  12. 2026Picking the Right Specialist: Attentive Neural Process-based Selection of Task-Specialized Models as Tools for Agentic Healthcare Systemscited by 2
10 more
  1. 2026Automatic Discovery of Disease Subgroups by Contrasting with Healthy Controlscited by 2
  2. 2026ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-trainingcited by 1
  3. 2025Evaluating Vision Language Models (VLMs) for Radiology: A Comprehensive Analysiscited by 0
  4. 2025DeltaMIL: Gated Memory Integration for Efficient and Discriminative Whole Slide Image Analysiscited by 0
  5. 2026CORA: Generalizable coronary artery disease assessment and risk stratification from coronary CT angiography using pathology-centric representation learningcited by 0
  6. 2026Model-agnostic information transfer and fusion for classification with label noisecited by 0
  7. 2026Mirror-Fusion Attention for Reflection-Aware Self-Supervised Representation Learningcited by 0
  8. 2026Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translationcited by 0
  9. 2026Clinically-Grounded Hierarchical Classification for Consistent Chest X-ray Interpretationcited by 0
  10. 2026Mr.Dec: Daily-Scale Longitudinal Multimodal Modeling for 30-Day Readmission Predictioncited by 0