D5 - Double adversarial domain adaptation for whole-slide-imageclassification
Yuchen Yang, Amir Akbarnejad, Nilanjan Ray, Gilbert Bigras
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.
Wednesday 7th July
D4-12 (short): Detection and Diagnosis 1 - 16:45 - 17:30 (UTC+2)