The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
The Gleason score suffers from significant inter-observer variability. This problem could be solved by the fully automated deep learning system developed by Wouter Bulten and his colleagues. Their work appeared online today in The Lancet Oncology. Read more →
The KNAW Early Career Award is intended to showcase talented PhD graduates, and to provide them with support and encouragement. The Award is aimed at researchers at the start of their career who are capable of developing innovative and original research ideas. 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 this project is to develop a deep learning model that can detect and classify the different types of artifacts observed in whole slide images.
The goal is to develop a deep learning algorithm for the differentiation of STIC lesions from normal fallopian tube epithelium. The digital pathology images of a cohort of both STIC lesions and normal tubal epithelium from BRCA1/2 carries will be available. The output will be an algorithm that can identify aberrant tubal epithelium with a high sensitivity.
The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.
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 →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.
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