D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification

TL;DR

D-Cube is a novel diffusion model-based approach that uses hyper-features and contrastive learning to improve the accuracy and robustness of cancer diagnosis in medical imaging, especially under data limitations.

Contribution

It introduces a new diffusion-driven feature selection method that enhances classification performance in medical imaging with limited or imbalanced data.

Findings

Outperforms baseline models across multiple imaging modalities

Shows robustness in data-limited and imbalanced scenarios

Achieves state-of-the-art diagnostic accuracy

Abstract

The integration of deep learning technologies in medical imaging aims to enhance the efficiency and accuracy of cancer diagnosis, particularly for pancreatic and breast cancers, which present significant diagnostic challenges due to their high mortality rates and complex imaging characteristics. This paper introduces Diffusion-Driven Diagnosis (D-Cube), a novel approach that leverages hyper-features from a diffusion model combined with contrastive learning to improve cancer diagnosis. D-Cube employs advanced feature selection techniques that utilize the robust representational capabilities of diffusion models, enhancing classification performance on medical datasets under challenging conditions such as data imbalance and limited sample availability. The feature selection process optimizes the extraction of clinically relevant features, significantly improving classification accuracy and demonstrating resilience in imbalanced and limited data scenarios. Experimental results validate the effectiveness of D-Cube across multiple medical imaging modalities, including CT, MRI, and X-ray, showing superior performance compared to existing baseline models. D-Cube represents a new strategy in cancer detection, employing advanced deep learning techniques to achieve state-of-the-art diagnostic accuracy and efficiency.