# Chest X-ray Report Generation through Fine-Grained Label Learning

arXiv: https://arxiv.org/abs/2007.13831 · HTML: https://mlsanity.com/paper/2007.13831/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 26 of 26)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment](https://mlsanity.com/paper/2112.15011/) (2021)
- [Contrastive Attention for Automatic Chest X-ray Report Generation](https://mlsanity.com/paper/2106.06965/) (2021)
- [AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation](https://mlsanity.com/paper/2203.10095/) (2022)
- [Automated Radiology Report Generation: A Review of Recent Advances](https://mlsanity.com/paper/2405.10842/) (2024)
- [RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting](https://mlsanity.com/paper/2107.02104/) (2021)
- [Medical Image Captioning via Generative Pretrained Transformers](https://mlsanity.com/paper/2209.13983/) (2022)
- [Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting](https://mlsanity.com/paper/2307.05766/) (2023)
- [Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation](https://mlsanity.com/paper/2207.01208/) (2022)
- [DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis](https://mlsanity.com/paper/2211.13229/) (2022)
- [A Survey of Medical Vision-and-Language Applications and Their Techniques](https://mlsanity.com/paper/2411.12195/) (2024)
- [Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation](https://mlsanity.com/paper/2209.01604/) (2022)
- [Fact-Checking of AI-Generated Reports](https://mlsanity.com/paper/2307.14634/) (2023)
- [Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting](https://mlsanity.com/paper/2308.15961/) (2023)
- [FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays](https://mlsanity.com/paper/2203.15723/) (2022)
- [On the Importance of Image Encoding in Automated Chest X-Ray Report Generation](https://mlsanity.com/paper/2211.13465/) (2022)
- [Evaluating Automated Radiology Report Quality through Fine-Grained Phrasal Grounding of Clinical Findings](https://mlsanity.com/paper/2412.01031/) (2024)
- [RadSEM: A Finding-by-Finding Metric for Clinical Consistency in Radiology Reports](https://mlsanity.com/paper/2606.17062/) (2026)
- [AI assisted method for efficiently generating breast ultrasound screening reports](https://mlsanity.com/paper/2107.13431/) (2021)
- [Exploration and Exploitation of Unlabeled Data for Open-Set Semi-Supervised Learning](https://mlsanity.com/paper/2306.17699/) (2023)
- [Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports](https://mlsanity.com/paper/2509.21356/) (2025)
- [Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architectures](https://mlsanity.com/paper/2205.02841/) (2022)
- [Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion](https://mlsanity.com/paper/2504.03135/) (2025)
- [CorBenchX: Large-Scale Chest X-Ray Error Dataset and Vision-Language Model Benchmark for Report Error Correction](https://mlsanity.com/paper/2505.12057/) (2025)
- [Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays](https://mlsanity.com/paper/2204.09676/) (2022)
- [Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning](https://mlsanity.com/paper/2207.14202/) (2022)

## Abstract (arXiv)

Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the quality of reports generated by current automated approaches is not yet clinically acceptable as they cannot ensure the correct detection of a broad spectrum of radiographic findings nor describe them accurately in terms of laterality, anatomical location, severity, etc. In this work, we present a domain-aware automatic chest X-ray radiology report generation algorithm that learns fine-grained description of findings from images and uses their pattern of occurrences to retrieve and customize similar reports from a large report database. We also develop an automatic labeling algorithm for assigning such descriptors to images and build a novel deep learning network that recognizes both coarse and fine-grained descriptions of findings. The resulting report generation algorithm significantly outperforms the state of the art using established score metrics.