SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging
Salah Eddine Bekhouche, Gaby Maroun, Fadi Dornaika, Abdenour Hadid
2025-07-25
Summary
This paper talks about SegDT, a new model that uses a combination of diffusion transformers and a technique called Rectified Flow to accurately segment skin lesions in medical images.
What's the problem?
Segmenting medical images like skin lesions is difficult because it requires high precision and speed for practical use in healthcare, but existing models often struggle with accuracy or are too slow.
What's the solution?
The researchers designed SegDT to use diffusion transformers along with Rectified Flow, which helps the model improve segmentation quality while making the process faster. This combination allows the model to better focus on important details in images.
Why it matters?
This matters because SegDT can help doctors detect and analyze skin lesions more reliably and quickly, improving medical diagnosis and treatment planning.
Abstract
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.