冠状动脉造影狭窄检测：优先召回率而非精确率，如何做与如何评估
Stenosis detection in coronary angiograms, need recall over precision [D]
有开发者启动冠状动脉造影狭窄候选检测项目，目标为高召回率，允许误报但不能标记半张图像。其此前用 YOLO 在自有数据集上测试，域内表现尚可但换数据集后失效，部分原因是标注格式不一致：一套用固定尺寸框标点，另一套贴合病灶。他计划沿血管做半径剖面标记狭窄、再用网络过滤候选，并困惑于医生标注不完整导致召回率偏乐观的评估问题。
Starting a project to detect candidate stenoses in x-ray coronary angiograms. The goal is high recall, false positives are fine as long as it doesn't flag half the image.
I already tried YOLO on a couple of in-house datasets earlier. It looked fine in-domain and fell apart on the other dataset, and a big chunk of that turned out to be annotation format. One set had fixed-size boxes around a point, the other had boxes fitted to the lesion. Same disease, basically two different tasks.
I also have vessel segmentation and centerlines, so my first instinct is a radius profile along the vessel to flag narrowing, then maybe a network to filter the candidates. Has anyone actually done something like that, or is there a smarter way in?
The part that bugs me most is evaluation. The labels come from doctors and they obviously don't mark every narrowing, so any recall I compute against them is probably too optimistic. How do you get an honest number when the ground truth is incomplete?
Also open to papers, repos, or public datasets beyond ARCADE. And if you think I'm approaching this wrong, say so.
submitted by /u/Future-Structure-296
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来源：r/MachineLearning · reddit.com