Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners

TL;DR

This paper introduces an asymmetric transfer learning method for image segmentation that adapts classifiers to different scanners using a weighted ensemble based on similarity measures, improving robustness across diverse datasets.

Contribution

The paper proposes a novel transfer learning classifier using asymmetric similarity measures to adapt to scanner differences without labeled data from new scanners.

Findings

Bag similarity measure is most robust across datasets.

Asymmetry in similarity improves classification performance.

Method achieves excellent results on multiple brain tissue and lesion segmentation datasets.

Abstract

Supervised learning has been very successful for automatic segmentation of images from a single scanner. However, several papers report deteriorated performances when using classifiers trained on images from one scanner to segment images from other scanners. We propose a transfer learning classifier that adapts to differences between training and test images. This method uses a weighted ensemble of classifiers trained on individual images. The weight of each classifier is determined by the similarity between its training image and the test image. We examine three unsupervised similarity measures, which can be used in scenarios where no labeled data from a newly introduced scanner or scanning protocol is available. The measures are based on a divergence, a bag distance, and on estimating the labels with a clustering procedure. These measures are asymmetric. We study whether the asymmetry can improve classification. Out of the three similarity measures, the bag similarity measure is the most robust across different studies and achieves excellent results on four brain tissue segmentation datasets and three white matter lesion segmentation datasets, acquired at different centers and with different scanners and scanning protocols. We show that the asymmetry can indeed be informative, and that computing the similarity from the test image to the training images is more appropriate than the opposite direction.