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Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach
UCBMIR outperformed previous studies, achieving a top 5 recall of 99% and 80% on BreaKHis and SICAPv2, respectively, using the first evaluation technique.
Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading
In particular, a tailored Convolutional Auto Encoder (CAE) is trained to reconstruct 128x128x3 patches of prostate cancer Whole Slide Images (WSIs) as a pretext task.
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