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

Shohei Hayashi and Bisser Raytchev and Toru Tamaki and Kazufumi Kaneda
2019 arXiv   pre-print
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
more » ... 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.
arXiv:1909.12612v1 fatcat:5uysbiowrnfiza6yqdiqydufei