mlsanityData to 7 Oct 2026

RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting

arXiv 2107.02104 ↗First posted 5 Jul 2021cs.CVcited by 34

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Abstract (arXiv)

Chest radiographs are one of the most common diagnostic modalities in clinical routine. It can be done cheaply, requires minimal equipment, and the image can be diagnosed by every radiologists. However, the number of chest radiographs obtained on a daily basis can easily overwhelm the available clinical capacities. We propose RATCHET: RAdiological Text Captioning for Human Examined Thoraces. RATCHET is a CNN-RNN-based medical transformer that is trained end-to-end. It is capable of extracting image features from chest radiographs, and generates medically accurate text reports that fit seamlessly into clinical work flows. The model is evaluated for its natural language generation ability using common metrics from NLP literature, as well as its medically accuracy through a surrogate report classification task. The model is available for download at: http://www.github.com/farrell236/RATCHET.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 16 of its 32 references that are in the map. First posted on arXiv 5 Jul 2021.

Builds on

16 of 32 references.

References · 16

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2016Densely Connected Convolutional Networkscited by 6,691
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  7. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
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  9. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 459
  10. 2018TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rayscited by 139
  11. 2019Clinically Accurate Chest X-Ray Report Generationcited by 138
  12. 2019Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generationcited by 101
4 more
  1. 2017NegBio: a high-performance tool for negation and uncertainty detection in radiology reportscited by 79
  2. 2020Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkitcited by 66
  3. 2019Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichmentcited by 60
  4. 2020Chest X-ray Report Generation through Fine-Grained Label Learningcited by 26

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 9; 2023: 15; 2024: 7; 2025: 1; 2026: 2.

34 papers.

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  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  3. 2023KiUT: Knowledge-injected U-Transformer for Radiology Report Generationcited by 60
  4. 2022Cross-modal Prototype Driven Network for Radiology Report Generationcited by 58
  5. 2024CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Datasetcited by 33
  6. 2022Explaining Chest X-ray Pathologies in Natural Languagecited by 27
  7. 2022Detailed Annotations of Chest X-Rays via CT Projection for Report Understandingcited by 24
  8. 2022CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generationcited by 21
  9. 2023The Impact of Imperfect XAI on Human-AI Decision-Makingcited by 21
  10. 2023Transformers in Healthcare: A Surveycited by 17
  11. 2023Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reportingcited by 17
  12. 2022A survey on attention mechanisms for medical applications: are we moving towards better algorithms?cited by 15
22 more
  1. 2023A Systematic Review of Deep Learning-based Research on Radiology Report Generationcited by 14
  2. 2023Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Trainingcited by 13
  3. 2024LLaVA Needs More Knowledge: Retrieval Augmented Natural Language Generation with Knowledge Graph for Explaining Thoracic Pathologiescited by 11
  4. 2024A Survey of Medical Vision-and-Language Applications and Their Techniquescited by 11
  5. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  6. 2024A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Datacited by 6
  7. 2022FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rayscited by 5
  8. 2023SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Imagescited by 5
  9. 2022On the Importance of Image Encoding in Automated Chest X-Ray Report Generationcited by 3
  10. 2024Learning To Guide Human Decision Makers With Vision-Language Modelscited by 3
  11. 2023Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rayscited by 2
  12. 2023MvCo-DoT:Multi-View Contrastive Domain Transfer Network for Medical Report Generationcited by 2
  13. 2024Quality Control for Radiology Report Generation Models via Auxiliary Auditing Componentscited by 2
  14. 2025Recurrent Visual Feature Extraction and Stereo Attentions for CT Report Generationcited by 2
  15. 2026Graph-Augmented Topological Internalization with Dual-Stream Classifiers for Medical Report Generationcited by 1
  16. 2023Learning to Guide Human Experts via Personalized Large Language Modelscited by 0
  17. 2023Dynamic Multi-Domain Knowledge Networks for Chest X-ray Report Generationcited by 0
  18. 2023C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer networkcited by 0
  19. 2023TLMCM Network for Medical Image Hierarchical Multi-Label Classificationcited by 0
  20. 2023CAManim: Animating end-to-end network activation mapscited by 0
  21. 2024Automated Description Generation of Cytologic Findings for Lung Cytological Images Using a Pretrained Vision Model and Dual Text Decoders: Preliminary Studycited by 0
  22. 2026Transformer-Based Language Models Across Domain Verticals: Architectures, Applications and Critical Assessmentcited by 0