Diffusion-Based Approaches in Medical Image Generation and Analysis

TL;DR

This paper explores the use of diffusion models to generate synthetic medical images for training CNNs, showing promising results across multiple domains and potentially reducing reliance on patient data.

Contribution

It demonstrates that diffusion-generated synthetic images can effectively train CNNs for medical classification tasks, highlighting a new approach to address data scarcity.

Findings

CNNs trained on synthetic data achieve high accuracy on real data

Diffusion models produce realistic and relevant synthetic medical images

LIME analysis confirms models focus on meaningful features

Abstract

Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions remain about the performance of convolutional neural network (CNN) models on original and synthetic datasets. If diffusion-generated samples can help CNN models perform comparably to those trained on original datasets, reliance on patient-specific data for training CNNs might be reduced. In this study, we investigated the effectiveness of diffusion models for generating synthetic medical images to train CNNs in three domains: Brain Tumor MRI, Acute Lymphoblastic Leukemia (ALL), and SARS-CoV-2 CT scans. A diffusion model was trained to generate synthetic datasets for each domain. Pre-trained CNN architectures were then trained on these synthetic datasets and evaluated on unseen real data. All three datasets achieved promising classification performance using CNNs trained on synthetic data. Local Interpretable Model-Agnostic Explanations (LIME) analysis revealed that the models focused on relevant image features for classification. This study demonstrates the potential of diffusion models to generate synthetic medical images for training CNNs in medical image analysis.