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Robust Classification of Histology Images Exploiting Adversarial Auto Encoders
Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN
1557170X
Date Issued
2021-01-01
Author(s)
Kurian, Nikhil Cherian
Singh, Gurparkash
Hebbar, Poorvi
Kodate, Shreekanya
Rane, Swapnil
Sethi, Amit
Abstract
Deep learning (DL) thrives on the availability of a large number of high quality images with reliable labels. Due to the large size of whole slide images in digital pathology, patches of manageable size are often mined for use in DL models. These patches are variable in quality, weakly supervised, individually less informative, and noisily labelled. To improve classification accuracy even with these noisy inputs and labels in histopathology, we propose a novel method for robust feature generation using an adversarial autoencoder (AAE). We utilize the likelihood of the features in the latent space of AAE as a criterion to weigh the training samples. We propose different weighting schemes for our framework and evaluate the effectiveness of our methods on the publically available BreakHis and BACH histopathology datasets. We observe consistent improvement in AUC scores using our methods, and conclude that robust supervision strategies should be further explored for computational pathology.