SegDT: A Diffusion Transformer-Based Segmentation Model for Medical
  Imaging
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}.
68833580846d2e78b7548b98
Salah Eddine Bekhouche
claimed_verified
2025-07-25T08:38:18.314Z
63e7493fdb40d9e67feaccdd
https://cdn-avatars.huggingface.co/v1/production/uploads/63e7493fdb40d9e67feaccdd/9d1iTreC4GlxSiWvLT0dP.jpeg
Salah Eddine Bekhouche
user
Bekhouche
68833580846d2e78b7548b99
Gaby Maroun
68833580846d2e78b7548b9a
Fadi Dornaika
68833580846d2e78b7548b9b
Abdenour Hadid
SegDT: A Diffusion Transformer-Based Segmentation Model for Medical
  Imaging
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}.
SegDT, a diffusion transformer-based segmentation model with Rectified Flow, achieves state-of-the-art results in skin lesion segmentation with fast inference speeds, suitable for real-world medical applications.
diffusion transformer Rectified Flow medical image segmentation skin lesions benchmarking datasets inference speeds