Double adversarial domain adaptation for whole-slide-imageclassification

Yuchen Yang, Amir Akbarnejad, Nilanjan Ray, Gilbert Bigras

Keywords: whole-slide-image, domain adaptation, classification, deep learning.

TL;DR: A domain adaptation solution to WSI classfication

Abstract:

Image classification on whole-slide-image (WSI) is a challenging task. A previous work based on Fisher vector encoding provided a novel end-to-end pipeline with promising accuracy and computational efficiency.
However, this pipeline suffers from an accuracy drop when deployed to another dataset to perform the same task.
This poses a limitation on the practical use of the pipeline especially when the diagnoses of WSIs are hard to obtain.
This paper aims at providing a solution to mitigate the accuracy drop by using an unsupervised domain adaptation approach.
We propose to insert the domain classifiers into the pipeline in two stages to align the features during training.
We evaluate accuracy by calculating the confusion matrices before and after the adaptation on two datasets. We demonstrate that placing domain classifiers in different stages will boost accuracy.

Paper Type: methodological development

Primary Subject Area: Transfer Learning and Domain Adaptation

Secondary Subject Area: Detection and Diagnosis

Paper Status: original work, not submitted yet

Source Code Url: https://github.com/yuchen2580/double_adaptation_WSI

Data Set Url:

For the target dataset, we use the data from warwick contest: https://warwick.ac.uk/fac/cross_fac/tia/data/her2contest/
The source dataset is from Alberta CCI which is not released to the public at this moment and awaiting approvals.

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