Projects

AMI

Joint project between DIAG and Fraunhofer MEVIS aimed at the development of a generic platform for automatic medical image analysis.

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AQUILA

The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.

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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.

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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.

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Deep PCA

In this project, we will combine deep learning and digitized whole-slide imaging of prostate cancer for reproducible extraction of quantitative biomarkers.

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Multimot

MULTIMOT aims to build an open data ecosystem for cell migration research, through standardization, dissemination and meta-analysis efforts.

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PRIM4BC

Detecting biomarkers for improved prognosis for triple negative breast cancer by combining histopathology, multiplex immunohistochemistry and Deep Learning.

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STITPRO II

Project with focus on the extension of open-source software ASAP that includes functionality for study and case management.

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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.

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Tumor-Stroma Ratio

We aim at developing automatic and reproducible quantification of Tumor-Stroma Ratio in Whole-Slide Images using Deep Learning.

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Tumor budding

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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Finished projects

CAMELYON16

ISBI 2016 challenge on cancer metastases detection in lymph node.

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STITPRO I

Project on the development of ASAP and on the automatic detection of metastases in WSI of sentinel lymph nodes of breast cancer patients.

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VPH-PRISM

Study on the correlations between several radiological modalities and histpathology for more understanding of breast cancer presentation.

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