Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images

TL;DR

This paper introduces a novel deep learning algorithm for biomedical image segmentation that employs a retina-like sequential attention mechanism, effectively focusing on difficult subareas with limited training data.

Contribution

It presents a new attention mechanism inspired by the retina, improving segmentation accuracy with few training images in biomedical applications.

Findings

Outperforms existing patch-based CNN methods.

Utilizes ensemble learning for better accuracy.

Effective with limited training data.

Abstract

In this paper we propose a novel deep learning-based algorithm for biomedical image segmentation which uses a sequential attention mechanism able to shift the focus of attention across the image in a selective way, allowing subareas which are more difficult to classify to be processed at increased resolution. The spatial distribution of class information in each subarea is learned using a retina-like representation where resolution decreases with distance from the center of attention. The final segmentation is achieved by averaging class predictions over overlapping subareas, utilizing the power of ensemble learning to increase segmentation accuracy. Experimental results for semantic segmentation task for which only a few training images are available show that a CNN using the proposed method outperforms both a patch-based classification CNN and a fully convolutional-based method.