用扩散模型提升医学图像分割精度，解决边界模糊与小病灶漏检问题。
Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models
- 结合扩散模型的迭代去噪机制增强细节捕捉能力
- 在低对比度和小目标场景下分割效果优于传统深度学习方法
- 适合临床诊断中对高精度分割有需求的研究者使用
医学图像分割对精准临床诊断至关重要，但面临病灶与正常组织对比度低、边界不清及患者间差异大等挑战。深度学习虽提升了分割准确率与效率，仍高度依赖专家标注，且受限于医学图像数据集规模小、获取成本高。扩散模型凭借其迭代去噪过程，在细节保留方面展现出潜力，但对小目标分割和边界精度仍存困难。本文探讨了医学图像分割的重要性，分析当前深度学习方法的局限性，并展望扩散模型在克服这些挑战中的应用前景。
原文摘要 · Abstract (English)
Medical image segmentation is crucial for accurate clinical diagnoses, yet it faces challenges such as low contrast between lesions and normal tissues, unclear boundaries, and high variability across patients. Deep learning has improved segmentation accuracy and efficiency, but it still relies heavily on expert annotations and struggles with the complexities of medical images. The small size of medical image datasets and the high cost of data acquisition further limit the performance of segmentation networks. Diffusion models, with their iterative denoising process, offer a promising alternative for better detail capture in segmentation. However, they face difficulties in accurately segmenting small targets and maintaining the precision of boundary details. This article discusses the importance of medical image segmentation, the limitations of current deep learning approaches, and the potential of diffusion models to address these challenges.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。