  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.
