Projects & Software
Current research 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 →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 →Software
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.
Automated Gleason Grading
Automated analysis of prostate biopsies following the Gleason grading system.
HookNet
HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
Neural Image Compression
Neural image compression for gigapixel histopathology image analysis.
Current student projects
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 →Finished projects
AMI
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.
Read more →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.
Read more →VPH-PRISM
Study on the correlations between several radiological modalities and histpathology for more understanding of breast cancer presentation.
Read more →