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

TL;DR

This study introduces a large-scale radio-pathomics dataset and a hierarchical deep learning classification system for pulmonary lesions, improving accuracy and comprehensiveness in non-invasive lung disease diagnosis.

Contribution

It presents the Pulmonary-RadPath dataset with over 5,000 CT images and develops a novel Leaky Dense Hierarchy method for hierarchical classification of lung diseases.

Findings

Outperforms prior methods in accuracy and data scale

Demonstrates effectiveness of hierarchical classification approach

Enables non-invasive diagnosis with high reliability

Abstract

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.