One-class methods DSVDD and DROC outperform MIL baselines for instance-level detection of rare malignant cells at witness rates ≤1% on bone marrow and oral cytology datasets.
Deepset simclr: Self-supervised deep sets for improved pathology representation learning.Pattern Recognition Letters, 186:64–70
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Needle in a Haystack: One-Class Representation Learning for Detecting Rare Malignant Cells in Computational Cytology
One-class methods DSVDD and DROC outperform MIL baselines for instance-level detection of rare malignant cells at witness rates ≤1% on bone marrow and oral cytology datasets.