End-to-end learning for detecting MYC translocationsDownload PDF

22 Apr 2022, 09:59 (edited 04 Jun 2022)MIDL 2022 Short PapersReaders: Everyone
  • Keywords: MYC, DLBCL, End-to-end learning, whole slide image classification
  • TL;DR: We use an end-to-end whole slide classification algorithm to classify MYC translocations in H&E slides of DLBCL
  • Abstract: Recent developments have improved whole-slide image classification to the point where the entire slide can be analyzed using only weak labels, whilst retaining both local and global context. In this paper, we use an end-to-end whole-slide image classification approach using weak labels to classify MYC translocations in slides of diffuse large B-cell lymphoma. Our model is able to achieve an AUC of 0.8012, which indicates the possibility of learning relevant features for MYC translocations.
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  • Paper Type: novel methodological ideas without extensive validation
  • Primary Subject Area: Application: Histopathology
  • Secondary Subject Area: Learning with Noisy Labels and Limited Data
  • 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.
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