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

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