Abstract
Acquiring pixel-level annotation has been a major challenge for machine learning methods in medical image analysis. Such difficulty mainly comes from two sources: localization requiring high expertise, and delineation requiring tedious and time-consuming work. Existing methods of easing the annotation effort mostly focus on the latter one, the extreme of which is replacing the delineation with a single label for all cases. We postulate that under a clinical-realistic setting, such methods alone may not always be effective in reducing the annotation requirements from conventional classification/detection algorithms, because the major difficulty can come from localization, which is often neglected but can be critical in medical domain, especially for histopathology images. In this work, we performed a worst-case scenario study to identify the information loss from missing detection. To tackle the challenge, we 1) proposed a different annotation strategy to image data with different levels of disease severity, 2) combined semi- and self-supervised representation learning with probabilistic weakly supervision to make use of the proposed annotations, and 3) illustrated its effectiveness in recovering useful information under the worst-case scenario. As a shift from previous convention, it can potentially save significant time for experts’ annotation for AI model development.
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Xu, Z. et al. (2022). Clinical-Realistic Annotation for Histopathology Images with Probabilistic Semi-supervision: A Worst-Case Study. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_8
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