mlsanityData to 14 Oct 2026

UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification

arXiv 2312.11038 ↗First posted 18 Dec 2023cs.CV cs.LGcited by 21

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition of natural domains and shown promise in medical imaging diagnosis for the Chest X-Rays (CXRs). However, current works mainly pay attention to the exploration on single dataset of CXRs, which locks the potential of this powerful paradigm on larger hybrid of multi-source CXRs datasets. We identify that although blending samples from the diverse sources offers the advantages to improve the model generalization, it is still challenging to maintain the consistent superiority for the task of each source due to the existing heterogeneity among sources. To handle this dilemma, we design a Conquer-and-Divide pre-training framework, termed as UniChest, aiming to make full use of the collaboration benefit of multiple sources of CXRs while reducing the negative influence of the source heterogeneity. Specially, the ``Conquer" stage in UniChest encourages the model to sufficiently capture multi-source common patterns, and the ``Divide" stage helps squeeze personalized patterns into different small experts (query networks). We conduct thorough experiments on many benchmarks, e.g., ChestX-ray14, CheXpert, Vindr-CXR, Shenzhen, Open-I and SIIM-ACR Pneumothorax, verifying the effectiveness of UniChest over a range of baselines, and release our codes and pre-training models at https://github.com/Elfenreigen/UniChest.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 25 of its 48 references that are in the map. First posted on arXiv 18 Dec 2023.

Builds on

25 of 48 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training24 May

Other references · 24

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  5. 2021Align before Fuse: Vision and Language Representation Learning with Momentum Distillationcited by 1,619
  6. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  7. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
  8. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  9. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  10. 2020Contrastive Learning of Medical Visual Representations from Paired Images and Textcited by 641
  11. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 597
  12. 2022MedCLIP: Contrastive Learning from Unpaired Medical Images and Textcited by 570
12 more
  1. 2022InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutionscited by 482
  2. 2020Asymmetric Loss For Multi-Label Classificationcited by 381
  3. 2022Making the Most of Text Semantics to Improve Biomedical Vision--Language Processingcited by 339
  4. 2021RadGraph: Extracting Clinical Entities and Relations from Radiology Reportscited by 319
  5. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  6. 2020VinDr-CXR: An open dataset of chest X-rays with radiologist's annotationscited by 239
  7. 2023MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiologycited by 149
  8. 2023Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Imagescited by 128
  9. 2020ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalitiescited by 38
  10. 2022On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyondcited by 26
  11. 2021Contrastive Language-Image Pre-training for the Italian Languagecited by 19
  12. 2020Darwin's Neural Network: AI-based Strategies for Rapid and Scalable Cell and Coronavirus Screeningcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 11; 2026: 3.

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  1. 2024Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learningcited by 18
  2. 2024Source Matters: Source Dataset Impact on Model Robustness in Medical Imagingcited by 10
  3. 2024DeViDe: Faceted medical knowledge for improved medical vision-language pre-trainingcited by 10
  4. 2025CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-raycited by 10
  5. 2025Long-tailed Recognition with Model Rebalancingcited by 10
  6. 2025Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Reviewcited by 9
  7. 2025Foundation Models in Medical Imaging: A Review and Outlookcited by 8
  8. 2024Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classificationcited by 7
  9. 2025Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attentioncited by 5
  10. 2025Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Modelscited by 5
  11. 2024LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Modelscited by 4
  12. 2024Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretationcited by 4
9 more
  1. 2025Differential-informed Sample Selection Accelerates Multimodal Contrastive Learningcited by 3
  2. 2025Boosting Medical Visual Understanding From Multi-Granular Language Learningcited by 2
  3. 2024MeDSLIP: Medical Dual-Stream Language-Image Pre-training with Pathology-Anatomy Semantic Alignmentcited by 1
  4. 2025RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosiscited by 1
  5. 2025Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Datacited by 1
  6. 2025Prompt Mechanisms in Medical Imaging: A Comprehensive Surveycited by 0
  7. 2026Auditing Medical Vision-Language Models on Chest Radiographs: Estimating Reference Agreement Across Institutionscited by 0
  8. 2026AlphaRAD: Grounded Zero-Shot Classification in Chest Radiology via α-Corrected Binary Cross Entropy and Factorized Latent Supervisioncited by 0
  9. 2026Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays
  2. 2025Benchmarking Chest X-ray Diagnosis Models Across Multinational Datasets
  3. 2023CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training
  4. 2026Med-AR: Autoregressive Vision-Language Pretraining for Long-Tailed Chest X-Ray Classification and Uncertainty-Aware Evaluation
  5. 2026SentZero: An Enhanced Sentence-Centric Vision-Language Pretraining for Multi-Task Zero-Shot Chest X-Ray Analysis
  6. 2025XBench: A Comprehensive Benchmark for Visual-Language Explanations in Chest Radiography
  7. 2026CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
  8. 2026HarMoE: Multi-Source Chest Radiograph Pretraining with Dataset-Disentangled Experts