# Extracting and Learning Fine-Grained Labels from Chest Radiographs

arXiv: https://arxiv.org/abs/2011.09517 · HTML: https://mlsanity.com/paper/2011.09517/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [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)
- [Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports](https://mlsanity.com/paper/2509.21356/) (2025)
- [Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context](https://mlsanity.com/paper/2509.19671/) (2025)
- [Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays](https://mlsanity.com/paper/2204.09676/) (2022)

## Abstract (arXiv)

Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray datasets to develop deep learning models for recognition of a handful of coarse finding classes such as opacities, masses and nodules. In this paper, we focus on extracting and learning fine-grained labels for chest X-ray images. Specifically we develop a new method of extracting fine-grained labels from radiology reports by combining vocabulary-driven concept extraction with phrasal grouping in dependency parse trees for association of modifiers with findings. A total of 457 fine-grained labels depicting the largest spectrum of findings to date were selected and sufficiently large datasets acquired to train a new deep learning model designed for fine-grained classification. We show results that indicate a highly accurate label extraction process and a reliable learning of fine-grained labels. The resulting network, to our knowledge, is the first to recognize fine-grained descriptions of findings in images covering over nine modifiers including laterality, location, severity, size and appearance.