A novel method for unsupervised scanner-invariance with DCAE model

Andrew Moyes, Kun Zhang, Liping Wang, Ming Ji, Daniel Crookes, Huiyu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Automated analysis of histopathology whole-slide images is impeded by the scanner dependent variance introduced in the slide scanning process. This work presents a novel dual-channel auto-encoder based model with a multi-component loss which learns a scanner-invariant representation of histopathology images. The learned representation can be used for a number of histopathology-related applications where images are captured from different scanners such as nuclei detection and cancer segmentation. The approach is validated on a set of lung tissue sub-images extracted from whole slide images. This method achieves a 50% improvement in SSIM score on tissue masks derived
from the learned representation compared to related methods. To the best of the author’s knowledge, this is the first work which explicitly learns a scanner-invariant representation of histopathology images from multiple domains simultaneously without labelled data or expensive preprocessing techniques.
Original languageEnglish
Title of host publicationBritish Machine Vision Conference (BMVC 2018)
Place of PublicationNewcastle, UK
PublisherBMVC
Number of pages13
Publication statusPublished - 03 Sep 2018

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    Moyes, A., Zhang, K., Wang, L., Ji, M., Crookes, D., & Zhou, H. (2018). A novel method for unsupervised scanner-invariance with DCAE model. In British Machine Vision Conference (BMVC 2018) BMVC . http://bmvc2018.org/callforpapers.html