Best Poster Award MIDL 2019
Congratulations to Thomas de Bel for winning the Best Poster Award at the second edition of the International Conference on Medical Imaging with Deep Learning held in London this week. Read more →
Computational Pathology Group
The Computational Pathology Group develops, validates and deploys novel medical image analysis methods based on deep learning technology and focusing on computer-aided diagnosis. Application areas include diagnostics and prognostics of breast, prostate and colon cancer. We have rapidly expanded over the last few years, counting over 15 people today. Our group is among the international front runners in the field, witnessed for instance by our highly successful CAMELYON challenges. We have a strong translational focus, facilitated by our close collaboration with clinicians and industry.
Highlights
Final meeting AMI-project.
The final meeting of the AMI-project took place last week. The AMI-project was a close collaborative project between the Diagnostic Image Analysis Group and the Fraunhofer Institute for Digital Medicine MEVIS. With the development of a generic platform for automatic medical image analysis, the project was a succes. 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.
Student project
Deep Learning to detect artifacts in whole slide images
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.
Student project
Deep Learning for the differentiation of STIC lesions
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.
Student project
Deep Learning to predict lung cancer immune therapy response
Aim of the proposed project is to develop deep learning algorithms for the extraction of features from scanned H&E and immunohistochemically stained tissue sections from lung cancer which carry predictive value in the context of immune therapy treatment response.
Projects
AQUILA
The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.
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 →PRIM4BC
Detecting biomarkers for improved prognosis for triple negative breast cancer by combining histopathology, multiplex immunohistochemistry and Deep Learning.
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 →
