Projects & Software

Current research projects


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

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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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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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The aim of ExaMode is to collect training data with limited human interaction for the processing of exascale volumes of healthcare data.

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Optimal treatment prediction for early stage lung cancer

We aim to combine advanced CT and FDG-PET image analysis with computational pathology to predict the most optimal treatment for each individual patient.

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

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PANDA Challenge

Challenge on prostate cancer grading of biopsies using the Gleason grading system.

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

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The aim of PROACTING is to predict neoadjuvant chemotherapy treatment response from a single pre-operative core-needle biopsy of breast cancer tissue.

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

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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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ASAP - Fluid whole-slide image viewer

ASAP (Automated Slide Analysis Platform) is a fast and fluid viewer for digitized multi-resolution histopathology images. ASAP offers several tools to make annotations in an intuitive way. Dots, rectangles, polygons are all supported. ASAP allows on-slide visualization of image analysis and machine learning results such as segmentation masks with customizable lookup-tables.

More information Github repository

Automated Gleason Grading

Automated analysis of prostate biopsies following the Gleason grading system.

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HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

More information Github repository

Neural Image Compression

Neural image compression for gigapixel histopathology image analysis.

More information Github repository

Current student projects

Automated Quantification of Tumor-Infiltrating Lymphocytes

Developing an algorithmn that can automatically detect and segment tumor-infiltrating lymphocytes in breast cancer.

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Finished projects


Automation in Medical Imaging - a joint project between DIAG and Fraunhofer MEVIS aimed at the development of a generic platform for automatic medical image analysis.

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Artifact detection in digitized histopathology images

Development of a deep learning algorithm that can classify the different types of artifacts in whole slide images.

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ISBI 2016 challenge on cancer metastases detection in lymph node.

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

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