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Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding
[article]
2019
arXiv
pre-print
Convolutional neural networks can be trained to perform histology slide classification using weak annotations with multiple instance learning (MIL). However, given the paucity of labeled histology data, direct application of MIL can easily suffer from overfitting and the network is unable to learn rich feature representations due to the weak supervisory signal. We propose to overcome such limitations with a two-stage semi-supervised approach that combines the power of data-efficient
arXiv:1910.10825v3
fatcat:vm4m2w6oqfhdvearkxhtf6kg74