# Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study

arXiv: https://arxiv.org/abs/2010.04049 · HTML: https://mlsanity.com/paper/2010.04049/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 8 of 8)

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- [Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers](https://mlsanity.com/paper/2112.02353/) (2021)
- [Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation](https://mlsanity.com/paper/2307.15645/) (2023)
- [Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans](https://mlsanity.com/paper/2206.03049/) (2022)
- [hYOLO Model: Enhancing Object Classification with Hierarchical Context in YOLOv8](https://mlsanity.com/paper/2510.23278/) (2025)
- [Disentangling Lung-Cancer CT/LDCT AI: A Systematic Evidence Map of Clinical Tasks, Evidence Chains, and Translational Gaps](https://mlsanity.com/paper/2609.35240/) (2026)

## Abstract (arXiv)

Diagnosis of pulmonary lesions from computed tomography (CT) is important but challenging for clinical decision making in lung cancer related diseases. Deep learning has achieved great success in computer aided diagnosis (CADx) area for lung cancer, whereas it suffers from label ambiguity due to the difficulty in the radiological diagnosis. Considering that invasive pathological analysis serves as the clinical golden standard of lung cancer diagnosis, in this study, we solve the label ambiguity issue via a large-scale radio-pathomics dataset containing 5,134 radiological CT images with pathologically confirmed labels, including cancers (e.g., invasive/non-invasive adenocarcinoma, squamous carcinoma) and non-cancer diseases (e.g., tuberculosis, hamartoma). This retrospective dataset, named Pulmonary-RadPath, enables development and validation of accurate deep learning systems to predict invasive pathological labels with a non-invasive procedure, i.e., radiological CT scans. A three-level hierarchical classification system for pulmonary lesions is developed, which covers most diseases in cancer-related diagnosis. We explore several techniques for hierarchical classification on this dataset, and propose a Leaky Dense Hierarchy approach with proven effectiveness in experiments. Our study significantly outperforms prior arts in terms of data scales (6x larger), disease comprehensiveness and hierarchies. The promising results suggest the potentials to facilitate precision medicine.