SegDT: A Diffusion Transformer-Based Segmentation Model for Medical
  Imaging
Authors: name: Salah Eddine Bekhouche, name: Gaby Maroun, name: Fadi Dornaika, name: Abdenour Hadid

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
https://github.com/Bekhouche/SegDT{GitHub}.