Joint project between DIAG and Fraunhofer MEVIS aimed at the development of a generic platform for automatic medical image analysis.
The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.
ISBI 2017 challenge to evaluate algorithms for automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections.
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.
In this project, we will combine deep learning and digitized whole-slide imaging of prostate cancer for reproducible extraction of quantitative biomarkers.
MULTIMOT aims to build an open data ecosystem for cell migration research, through standardization, dissemination and meta-analysis efforts.
Detecting biomarkers for improved prognosis for triple negative breast cancer by combining histopathology, multiplex immunohistochemistry and Deep Learning.
Project with focus on the extension of open-source software ASAP that includes functionality for study and case management.
The investigation of the role of immune cell subsets in interstitial fibrosis and tubular atrophy in renal allografts, using multiplex immunohistochemistry and Deep Learning.
We aim at developing automatic and reproducible quantification of Tumor-Stroma Ratio in Whole-Slide Images using Deep Learning.
In this project, we will develop and validate digital image analysis algorithms for quantification of tumor budding from scanned whole slide images.
ISBI 2016 challenge on cancer metastases detection in lymph node.
Project on the development of ASAP and on the automatic detection of metastases in WSI of sentinel lymph nodes of breast cancer patients.
Study on the correlations between several radiological modalities and histpathology for more understanding of breast cancer presentation.