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Examode

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HE_adversarial_CNN

We published a library containing the implementation of the H&E-adversarial network, a convolutional neural network to learn stain-invariant features through Hematoxylin and Eosin regression. The library is associated with the paper “H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression“.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825292

This project is part of the BDV initiative

In liaison with

Recent posts xxx

  • The final version of the ExaMode ontology is available in Zenodo February 27, 2023
  • Semestrial meeting in Padua January 25, 2023
  • Gianmaria Silvello invited speaker for the second Swetaly workshop on AI (16.09.2022) described ExaMode and the latest advancements of the project September 16, 2022
  • The ExaMode results on “training computer-aided diagnosis models without human annotations” are on a spotlight on the Nature Health Portfolio! August 24, 2022
  • “Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations” is out on Digital Medicine (gold  open access). July 22, 2022
  • A new digital terminology open access book June 9, 2022
  • Training Biomedical Relation Extraction Models with a Limited amount of Annotated Data June 9, 2022
  • The latest ExaMode ontology version is available May 24, 2022
  • ExaMode at  the MARVEL Dataweek 2022 Workshop May 13, 2022
  • ExaMode and yourTerm March 11, 2022
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