# Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images Source: [Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:50:28.993Z Authors: Shohei Hayashi, Bisser Raytchev, Toru Tamaki, Kazufumi Kaneda Published: 2019-09-27T10:55:24.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()