SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging

TL;DR

SegDT is a diffusion transformer-based model for medical image segmentation that achieves state-of-the-art results with fast inference, suitable for real-world healthcare applications.

Contribution

Introduces SegDT, a novel diffusion transformer model that enhances medical image segmentation performance while maintaining low computational costs.

Findings

Achieves state-of-the-art segmentation accuracy on benchmark datasets.

Maintains fast inference speeds suitable for clinical use.

Demonstrates robustness across multiple medical imaging datasets.

Abstract

Medical image segmentation is crucial for many healthcare tasks, including disease diagnosis and treatment planning. One key area is the segmentation of skin lesions, which is vital for diagnosing skin cancer and monitoring patients. In this context, this paper introduces SegDT, a new segmentation model based on diffusion transformer (DiT). SegDT is designed to work on low-cost hardware and incorporates Rectified Flow, which improves the generation quality at reduced inference steps and maintains the flexibility of standard diffusion models. Our method is evaluated on three benchmarking datasets and compared against several existing works, achieving state-of-the-art results while maintaining fast inference speeds. This makes the proposed model appealing for real-world medical applications. This work advances the performance and capabilities of deep learning models in medical image analysis, enabling faster, more accurate diagnostic tools for healthcare professionals. The code is made publicly available at \href{https://github.com/Bekhouche/SegDT}{GitHub}.