AIRMEC: AI for refining the molecular endometrial cancer classification Publications
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      • Publications

      Notable work

      HECTOR (Nature Medicine 2024)HECTOR (Nature Medicine 2024)
      Multimodal deep learning to predict distant recurrence-free probability from digitized H&E tumour slide and tumour stage.
      Nature Medicine 2024
      im4MEC (Lancet Digital Health 2023)im4MEC (Lancet Digital Health 2023)
      Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images.
      Lancet Digital Health 2023

      Team

      Dr. Tjalling BosseDr. Tjalling Bosse
      Pathologist and endometrial cancer expert, Leiden University Medical Center
      Prof. Dr. Viktor KoelzerProf. Dr. Viktor Koelzer
      Pathologist and Digital Pathology Expert, University Hospital of Basel
      Dr. Nanda HorewegDr. Nanda Horeweg
      Assistant Professor Radiation Oncology, Leiden University Medical Center
      Sarah VolinskySarah Volinsky
      PhD student, Department of Pathology, Leiden University Medical Center
      Nikki van den BergNikki van den Berg
      PhD student, Department of Pathology, Leiden University Medical Center
      Jurriaan Barkey WolfJurriaan Barkey Wolf
      Software engineer, Department of Pathology, Leiden University Medical Center
      Sonali AndaniSonali Andani
      PhD Student, Department of Computer Science, ETH Zurich
      Dr. Maxime LafargeDr. Maxime Lafarge
      Postdoctoral Researcher, Department of Biomedical Engineering, University of Basel
      Lydia SchönpflugLydia Schönpflug
      PhD student, Department of Biomedical Engineering, University of Basel

      © AIRMEC team