用癌变分级信息作提示，提升腺体分割精度。
Gland Segmentation Using SAM With Cancer Grade as a Prompt
- 以癌变等级作为提示引导分割模型
- 在多个数据集上达到当前最佳性能
- 适合病理图像分析与临床辅助诊断研究者
癌变等级是判断癌症恶性程度的关键临床指标。精确的腺体分割有助于更准确地进行癌变分级。在机器学习中，可利用腺体的良性/恶性二分类信息作为提示，指导分割与分级任务。通过引入良性或恶性先验知识，模型能预判目标形态，从而提升分割效果。本文采用分段任意模型（Segment Anything Model）解决分割问题，充分发挥其提示功能，并对模型结构和训练策略进行适当调整。改进后的模型在微调基础上实现显著提升，取得当前最优结果。
原文摘要 · Abstract (English)
Cancer grade is a critical clinical criterion that can be used to determine the degree of cancer malignancy. Revealing the condition of the glands, a precise gland segmentation can assist in a more effective cancer grade classification. In machine learning, binary classification information about glands (i.e., benign and malignant) can be utilized as a prompt for gland segmentation and cancer grade classification. By incorporating prior knowledge of the benign or malignant classification of the gland, the model can anticipate the likely appearance of the target, leading to better segmentation performance. We utilize Segment Anything Model to solve the segmentation task, by taking advantage of its prompt function and applying appropriate modifications to the model structure and training strategies. We improve the results from fine-tuned Segment Anything Model and produce SOTA results using this approach.
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。