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

by   Jiancheng Yang, et al.

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.


Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases

The advancement of computer-aided detection systems had a significant im...

Faithful learning with sure data for lung nodule diagnosis

Recent evolution in deep learning has proven its value for CT-based lung...

Explainable Deep Learning Algorithm for Distinguishing Incomplete Kawasaki Disease by Coronary Artery Lesions on Echocardiographic Imaging

Background and Objective: Incomplete Kawasaki disease (KD) has often bee...

Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis

Radiomics analysis has achieved great success in recent years. However, ...

Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks

The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for e...

Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images

The computer-aided diagnosis of focal liver lesions (FLLs) can help impr...

Malignancy-Aware Follow-Up Volume Prediction for Lung Nodules

Follow-up serves an important role in the management of pulmonary nodule...

Please sign up or login with your details

Forgot password? Click here to reset