注释
分割
计算机科学
人工智能
可扩展性
过程(计算)
图像分割
机器学习
点(几何)
像素
模式识别(心理学)
监督学习
数字化病理学
人工神经网络
数学
操作系统
数据库
几何学
作者
Haijun Lei,Jia Zhao,Guanjie Tong,Xinyun Qiu,Hao Su,Baiying Lei
标识
DOI:10.1109/bibm58861.2023.10385927
摘要
Multiple Myeloma (MM) is a growing global health concern, and early diagnosis is crucial for effective treatment. Efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Microscopic images have high resolution, where cells are enormous and dense. Therefore, the annotation process is time-consuming and complex for tasks such as segmentation due to pixel-level marking. In this paper, we design an end-to-end weakly supervised myeloma cell segmentation framework based on point annotation. It can achieve accurate cell segmentation comparable to fully supervised methods while reducing the need for manual annotation, greatly shortening annotation time. Experimental results demonstrate that our method achieves 98% of its fully-supervised performance with only 10 annotated random points per instance, and outperforms the fully-supervised Mask RCNN.
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