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

Minhee Jang, Juheon Son, Thanaporn Viriyasaranon, Junho Kim, Jang-Hwan Choi

TL;DR

Diffusion-Driven Diagnosis (D-Cube) is introduced, a novel approach that leverages hyper-features from a diffusion model combined with contrastive learning to improve cancer diagnosis, showing superior performance compared to existing baseline models.

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.

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

TL;DR

Diffusion-Driven Diagnosis (D-Cube) is introduced, a novel approach that leverages hyper-features from a diffusion model combined with contrastive learning to improve cancer diagnosis, showing superior performance compared to existing baseline models.

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.

Paper Structure

This paper contains 31 sections, 10 equations, 2 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Medical Domain Adaptation
  4. Exploiting Diffusion Features
  5. D-Cube
  6. Preliminary: Diffusion Models
  7. Dual-Stage Training
  8. Step 1, Training a Diffusion Model
  9. Objective
  10. Step 2, Training a Classifier Model
  11. Feature Selection with a Gaussianity Metric
  12. Objective
  13. Additional Technique
  14. Experiments
  15. Datasets
  16. ...and 16 more sections

Figures (2)

  • Figure 1: Overall Architecture: Step 1 involves the process of generating diffusion features that enhance the performance of D-Cube, while step 2 entails training a D-Cube for cancer diagnosis using the features generated from frozen diffusion model. $x_t$ represents an original image $x_0$ with noise at random time step $t$. For more detailed information, refer to the method in Section \ref{['sec:dcube']} and the analysis in Fig. \ref{['fig:layer_metric']}. The dashed line indicates the optional use of sub features.
  • Figure 2: Gaussianity Test of Each Datasets This graph presents the p-value analysis for pancreas cacner CT, breast cancer MRI, and COVID chest X-ray. Yellow star denote the best combination used, while red cross marks represent the worst combination. The p-value is calculated based on the average of features with a batch size of 256 when t=100.