Fiets Pieten visited the Computational Pathology Group
The Fiets Pieten cycled a total of 355 km across the country, solving a special CPG mystery with help of their fellow crew members along the way. Read more →
Computational Pathology Group
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology.
News
VIDI grant for Francesco Ciompi for improving lung cancer treatment planning
Francesco Ciompi of the Computational Pathology group has received a prestigious NWO-TTW VIDI grant of 800,000 euro for his project "Predicting Lung Cancer Immunotherapy Response. It's personal". Read more →
Vacancies & Student projects
Student project
Deep Learning to predict recurrence in triple negative breast cancer (TNBC)
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.
Projects
AQUILA
The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.
Read more →Artifact detection in digitized histopathology images
Development of a deep learning algorithm that can classify the different types of artifacts in whole slide images.
Read more →CAMELYON17
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 →DCIS
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 →Deep PCA
In this project, we will combine deep learning and digitized whole-slide imaging of prostate cancer for reproducible extraction of quantitative biomarkers.
Read more →ExaMode
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
MULTIMOT aims to build an open data ecosystem for cell migration research, through standardization, dissemination and meta-analysis efforts.
Read more →PANDA Challenge
Challenge on prostate cancer grading of biopsies using the Gleason grading system.
Read more →PRIM4BC
Detecting biomarkers for improved prognosis for triple negative breast cancer by combining histopathology, multiplex immunohistochemistry and Deep Learning.
Read more →PROACTING
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 →STITPRO II
Project with focus on the extension of open-source software ASAP that includes functionality for study and case management.
Read more →SysMIFTA
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 →Tumor-Stroma Ratio
We aim at developing automatic and reproducible quantification of Tumor-Stroma Ratio in Whole-Slide Images using Deep Learning.
Read more →Tumor budding
In this project, we will develop and validate digital image analysis algorithms for quantification of tumor budding from scanned whole slide images.
Read more →
