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Snow Supervision in Digital Pathology: Managing Imperfect Annotations for Segmentation in Deep Learning
[post]
2020
unpublished
In digital pathology, image segmentation algorithms are usually ranked on clean, benchmark datasets. However, annotations in digital pathology are hard, time-consuming and by nature imperfect. We expand on the SNOW (Semi-, Noisy and/or Weak) supervision concept introduced in an earlier work to characterize such data supervision imperfections. We analyse the effects of SNOW supervision on typical DCNNs, and explore learning strategies to counteract those effects. We apply those lessons to the
doi:10.21203/rs.3.rs-116512/v1
fatcat:3huyg32z2vbrpmwk6n2iipk3pa