The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
In this project we aim to develop and validate AI techniques for the detection of serous tubal intra-epithelial carcinoma (STIC), a non-invasive lesion in the distal Fallopian tube which is expected to be a precursor for ovarian cancer. Read more →
Maschenka Balkenhol succesfully defended her PhD thesis titled 'Tissue-based biomarker assessment for predicting prognosis of triple negative breast cancer: the additional value of artificial intelligence' on the 15th of September. Read more →
Aim of the presently proposed ‘proof of concept‘ study is to develop digital pattern recognition algorithms (more specifically: deep neural networks) for the extraction of morphological features from scanned H&E stained tissue sections from TNBC which are indicative for the risk of recurrence.
The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.
Read more →Development of a deep learning algorithm that can classify the different types of artifacts in whole slide images.
Read more →ISBI 2017 challenge to evaluate algorithms for automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections.
Read more →The aim of this project is to use deep learning for histological assessment of the stroma for improved risk stratification of ductal carcinoma in situ (DCIS) patients.
Read more →In this project, we will combine deep learning and digitized whole-slide imaging of prostate cancer for reproducible extraction of quantitative biomarkers.
Read more →The aim of ExaMode is to collect training data with limited human interaction for the processing of exascale volumes of healthcare data.
Read more →MULTIMOT aims to build an open data ecosystem for cell migration research, through standardization, dissemination and meta-analysis efforts.
Read more →Challenge on prostate cancer grading of biopsies using the Gleason grading system.
Read more →Detecting biomarkers for improved prognosis for triple negative breast cancer by combining histopathology, multiplex immunohistochemistry and Deep Learning.
Read more →The aim of PROACTING is to predict neoadjuvant chemotherapy treatment response from a single pre-operative core-needle biopsy of breast cancer tissue.
Read more →Project with focus on the extension of open-source software ASAP that includes functionality for study and case management.
Read more →The investigation of the role of immune cell subsets in interstitial fibrosis and tubular atrophy in renal allografts, using multiplex immunohistochemistry and Deep Learning.
Read more →We aim at developing automatic and reproducible quantification of Tumor-Stroma Ratio in Whole-Slide Images using Deep Learning.
Read more →In this project, we will develop and validate digital image analysis algorithms for quantification of tumor budding from scanned whole slide images.
Read more →
