ECDP 2024
We recently heard that three scientific works from my group have been accepted at the European Congress on Digital Pathology (ECDP), which will be held in Vilnius (Lithuania) on 5-8 June 2024.
The following abstracts were accepted:
"Immunotherapy response prediction for non-small cell lung cancer is improved by using cell-graphs of the tumor microenvironment", work done by Leander van Eekelen, where he shows preliminary results on graphical models encoding characteristics of the tumor microenvironment in non-small cell lung cancer biopsies and how they correlate with the response to immune checkpoint inhibitors, improving upon current clinical practice that use the tumor proportion score.
"Vision Language Foundation Models for Scoring Tumor-Infiltrating Lymphocytes in Breast Cancer through Text Prompting", work done by Michelle Stegeman, where she shows preliminary work on how vision-language foundation models could be used to quantify features at the base of biomarkers such as tumour-infiltrating lymphocytes in breast cancer, solely using textual prompts as input.
"Automated Quality Control in Histopathology through Artifact Segmentation", work done by Marina D'Amato, where she shows how AI can be used to detect artefacts in digital pathology whole-slide images across multiple tissue types, staining (H&E and immunohistochemistry) and species (humans, rats, dogs, etc.). a key component for quality control in digital pathology for clinical and toxicological applications.
Congrats to the whole team!
The following abstracts were accepted:
"Immunotherapy response prediction for non-small cell lung cancer is improved by using cell-graphs of the tumor microenvironment", work done by Leander van Eekelen, where he shows preliminary results on graphical models encoding characteristics of the tumor microenvironment in non-small cell lung cancer biopsies and how they correlate with the response to immune checkpoint inhibitors, improving upon current clinical practice that use the tumor proportion score.
"Vision Language Foundation Models for Scoring Tumor-Infiltrating Lymphocytes in Breast Cancer through Text Prompting", work done by Michelle Stegeman, where she shows preliminary work on how vision-language foundation models could be used to quantify features at the base of biomarkers such as tumour-infiltrating lymphocytes in breast cancer, solely using textual prompts as input.
"Automated Quality Control in Histopathology through Artifact Segmentation", work done by Marina D'Amato, where she shows how AI can be used to detect artefacts in digital pathology whole-slide images across multiple tissue types, staining (H&E and immunohistochemistry) and species (humans, rats, dogs, etc.). a key component for quality control in digital pathology for clinical and toxicological applications.
Congrats to the whole team!