SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging
This work addresses the need for accurate and efficient diagnostic tools in healthcare, specifically for skin cancer diagnosis, though it appears incremental as it builds on existing diffusion transformer methods.
The paper tackles medical image segmentation, particularly for skin lesions, by introducing SegDT, a diffusion transformer-based model that achieves state-of-the-art results on three benchmarking datasets with fast inference speeds.
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}.