Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis

TL;DR

This paper introduces a probabilistic deep learning framework that combines radiomics and shape analysis to improve lesion diagnosis, providing controllable and explainable predictions with uncertainty estimation.

Contribution

It proposes a novel end-to-end trainable model integrating shape analysis and ambiguity modeling for more reliable lesion diagnosis.

Findings

Effective lung nodule diagnosis on LIDC-IDRI database

Enhanced interpretability through shape analysis and uncertainty modeling

Improved diagnostic consistency in ambiguous cases

Abstract

Radiomics analysis has achieved great success in recent years. However, conventional Radiomics analysis suffers from insufficiently expressive hand-crafted features. Recently, emerging deep learning techniques, e.g., convolutional neural networks (CNNs), dominate recent research in Computer-Aided Diagnosis (CADx). Unfortunately, as black-box predictors, we argue that CNNs are "diagnosing" voxels (or pixels), rather than lesions; in other words, visual saliency from a trained CNN is not necessarily concentrated on the lesions. On the other hand, classification in clinical applications suffers from inherent ambiguities: radiologists may produce diverse diagnosis on challenging cases. To this end, we propose a controllable and explainable {\em Probabilistic Radiomics} framework, by combining the Radiomics analysis and probabilistic deep learning. In our framework, 3D CNN feature is extracted upon lesion region only, then encoded into lesion representation, by a controllable Non-local Shape Analysis Module (NSAM) based on self-attention. Inspired from variational auto-encoders (VAEs), an Ambiguity PriorNet is used to approximate the ambiguity distribution over human experts. The final diagnosis is obtained by combining the ambiguity prior sample and lesion representation, and the whole network named $DenseSharp^{+}$ is end-to-end trainable. We apply the proposed method on lung nodule diagnosis on LIDC-IDRI database to validate its effectiveness.