YAMU: Yet Another Modified U-Net Architecture for Semantic SegmentationDownload PDF

09 Dec 2021, 12:25 (edited 22 Jun 2022)MIDL 2022Readers: Everyone
  • Keywords: Digital histopathology, deep learning, semantic segmentation, HistNet, UNet
  • TL;DR: An improved context aggregation architecture for semantic segmentation in histopathology images
  • Abstract: Digital histopathology images must be examined accurately and quickly as part of a pathologist's clinical procedure. For histopathology image segmentation, different variants of U-Net and fully convolutional networks (FCN) are state-of-the-art. HistNet or histopathology network for semantic labelling in histopathology images, for example, is one of them. We improve our previously proposed model HistNet in this paper by introducing new skip pathways to the decoder stage to aggregate multiscale features and incorporate a feature pyramid to keep the contextual information. In addition, to boost performance, we employ a deep supervision training technique. We show that not only does the proposed design outperform the baseline, but it also outperforms state-of-the-art segmentation architectures with much fewer parameters.
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  • Paper Type: methodological development
  • Primary Subject Area: Segmentation
  • Secondary Subject Area: Application: Histopathology
  • Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
  • Code And Data: The code is part of AIRAMATRIX PVT. LTD cancer assessment system, which is copyrighted. After we have received the necessary clearances, we will make the source code available for research purposes only on our website (http://airamatrix.com).
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