Attention-based Dynamic Subspace Learners for Medical Image Analysis

TL;DR

This paper introduces a novel attention-based dynamic subspace learning method for medical image analysis that adaptively learns multiple attribute-focused embeddings, improving clustering, retrieval, and segmentation performance.

Contribution

It proposes a dynamic, attention-guided subspace learning framework that automatically determines the number of learners and enhances interpretability in medical image analysis.

Findings

Achieves competitive clustering and retrieval results on benchmark datasets.

Outperforms state-of-the-art in segmentation accuracy using attention maps.

Provides visual explanations of features contributing to image clustering.

Abstract

Learning similarity is a key aspect in medical image analysis, particularly in recommendation systems or in uncovering the interpretation of anatomical data in images. Most existing methods learn such similarities in the embedding space over image sets using a single metric learner. Images, however, have a variety of object attributes such as color, shape, or artifacts. Encoding such attributes using a single metric learner is inadequate and may fail to generalize. Instead, multiple learners could focus on separate aspects of these attributes in subspaces of an overarching embedding. This, however, implies the number of learners to be found empirically for each new dataset. This work, Dynamic Subspace Learners, proposes to dynamically exploit multiple learners by removing the need of knowing apriori the number of learners and aggregating new subspace learners during training. Furthermore, the visual interpretability of such subspace learning is enforced by integrating an attention module into our method. This integrated attention mechanism provides a visual insight of discriminative image features that contribute to the clustering of image sets and a visual explanation of the embedding features. The benefits of our attention-based dynamic subspace learners are evaluated in the application of image clustering, image retrieval, and weakly supervised segmentation. Our method achieves competitive results with the performances of multiple learners baselines and significantly outperforms the classification network in terms of clustering and retrieval scores on three different public benchmark datasets. Moreover, our attention maps offer a proxy-labels, which improves the segmentation accuracy up to 15% in Dice scores when compared to state-of-the-art interpretation techniques.