SegDT：基於擴散變壓器的醫學影像分割模型

摘要

醫學影像分割對於許多醫療任務至關重要，包括疾病診斷和治療規劃。其中一個關鍵領域是皮膚病變的分割，這對於診斷皮膚癌和監測患者狀況極為重要。在此背景下，本文介紹了SegDT，這是一種基於擴散變換器（DiT）的新型分割模型。SegDT專為低成本硬體設計，並結合了整流流（Rectified Flow），這在減少推理步驟的同時提高了生成質量，並保持了標準擴散模型的靈活性。我們的方法在三個基準數據集上進行了評估，並與多個現有工作進行了比較，在保持快速推理速度的同時取得了最先進的結果。這使得所提出的模型在實際醫療應用中具有吸引力。這項工作提升了深度學習模型在醫學影像分析中的性能和能力，為醫療專業人員提供了更快、更準確的診斷工具。代碼已公開於https://github.com/Bekhouche/SegDT{GitHub}。

English

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：基於擴散變壓器的醫學影像分割模型

SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging

摘要

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