  Understanding the morphological structure of medical images and precisely
segmenting the region of interest or abnormality is an important task that can
assist in diagnosis. However, the unique properties of medical imaging make
clear segmentation difficult, and the high cost and time-consuming task of
labeling leads to a coarse-grained representation of ground truth. Facing with
these problems, we propose a novel Diffusion Transformer Segmentation (DTS)
model for robust segmentation in the presence of noise. We propose an
alternative to the dominant Denoising U-Net encoder through experiments
applying a transformer architecture, which captures global dependency through
self-attention. Additionally, we propose k-neighbor label smoothing, reverse
boundary attention, and self-supervised learning with morphology-driven
learning to improve the ability to identify complex structures. Our model,
which analyzes the morphological representation of images, shows better results
than the previous models in various medical imaging modalities, including CT,
MRI, and lesion images.
