Stain Isolation-based Guidance for Improved Stain TranslationDownload PDF

21 Apr 2022, 06:52 (edited 04 Jun 2022)MIDL 2022 Short PapersReaders: Everyone
  • Keywords: Digital Pathology, GANs, Guided Domain Translation, Immunofluorescence
  • Abstract: Unsupervised and unpaired domain translation using generative adversarial neural networks, and more precisely CycleGAN, is state of the art for the stain translation of histopathology images. It often, however, suffers from the presence of cycle-consistent but non structure-preserving errors. We propose an alternative approach to the set of methods which, relying on segmentation consistency, enable the preservation of pathology structures. Focusing on immunohistochemistry (IHC) and multiplexed immunofluorescence (mIF), we introduce a simple yet effective guidance scheme as a loss function that leverages the consistency of stain translation with stain isolation. Qualitative and quantitative experiments show the ability of the proposed approach to improve translation between the two domains
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  • Paper Type: novel methodological ideas without extensive validation
  • Primary Subject Area: Transfer Learning and Domain Adaptation
  • Secondary Subject Area: Application: Histopathology
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