# Clinically-Grounded Hierarchical Classification for Consistent Chest X-ray Interpretation

arXiv: https://arxiv.org/abs/2608.03016 · HTML: https://mlsanity.com/paper/2608.03016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [Coherent Hierarchical Multi-Label Classification Networks](https://mlsanity.com/paper/2010.10151/) (2020)
- [Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels](https://mlsanity.com/paper/2005.12734/) (2020)
- [EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learning](https://mlsanity.com/paper/2405.05237/) (2024)
- [Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays](https://mlsanity.com/paper/2206.12704/) (2022)
- [AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray](https://mlsanity.com/paper/2105.09937/) (2021)
- [Visually Consistent Hierarchical Image Classification](https://mlsanity.com/paper/2406.11608/) (2024)
- [Anatomy-Driven Pathology Detection on Chest X-rays](https://mlsanity.com/paper/2309.02578/) (2023)
- [Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies](https://mlsanity.com/paper/2009.05609/) (2020)
- [Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation](https://mlsanity.com/paper/2505.21190/) (2025)
- [Modeling Clinical Uncertainty in Radiology Reports: from Explicit Uncertainty Markers to Implicit Reasoning Pathways](https://mlsanity.com/paper/2511.04506/) (2025)
- [Clinically-Inspired Hierarchical Multi-Label Classification of Chest X-rays with a Penalty-Based Loss Function](https://mlsanity.com/paper/2502.03591/) (2025)

## Abstract (arXiv)

Accurate chest X-ray interpretation is inherently hierarchical. Clinical decisions depend not only on what abnormality is present but where it is situated, requiring reasoning from broad anatomical systems down to specific pathological findings. Yet existing automated systems largely treat this as a flat classification problem, failing to capture inter-level dependencies or enforce coherence between coarse and fine predictions. We propose CHASE (Classification with Hierarchical Analysis and Structured Enforcement), a unified single-stage framework that mirrors radiologists' coarse-to-fine reasoning through a clinically driven three-level taxonomy of 9 anatomical regions, 17 sub-regions, and 28 pathological findings. CHASE jointly optimizes multi-level supervision, cross-level probability alignment, and a hierarchy-violation penalty within a shared Vision Transformer backbone. This ensures that fine-grained findings are anatomically supported by their coarser-level context rather than predicted in isolation. Experiments demonstrate that CHASE outperforms flat and hierarchical baselines across all levels while achieving superior probabilistic hierarchy consistency, with level-wise attention maps confirming anatomically grounded predictions. Code is available at: https://github.com/yejix-ai/CHASE.