Projects & Software
Current research projects

BIGPICTURE
The goal of Bigpicture is to accelerate the development of AI in pathology by providing a large repository of high-quality annotated pathology data, accessible in a responsible, inclusive and sustainable way.
Read more →
Deep Derma
The aim of this project is to apply artificial intelligence to detect basal cell carcinoma.
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 →
IGNITE
The goal of IGNITE is to use automatic biomarker extraction with deep learning to predict the response of non-small cell lung cancer patients to immunotherapy.
Read more →
GENERATOR: 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.
Read more →
PathRad Fusion
The aim of this project is to integrate histopathological and radiological images to improve our understanding of disease diagnosis and progression in prostate cancer.
Read more →
STIC
The aim of STIC is to improve the diagnostics of precursor lesions to high grade serous carcinoma (HGSC), the most common and lethal form of ovarian cancer.
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 →
UNESCO
The aim of UNESCO is to study techniques for estimating uncertainty in computational pathology.
Read more →
UNIC
The aim of UNIC is to develop artificial intelligence methods to refine diffuse-type gastric cancer (DGC) diagnostics.
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

Identification of features in benign breast disease biopsies that predict breast cancer risk
Development of a deep learning system to predict BC risk in H&E
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 →
AQUILA
The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.
Read more →
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 →
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 →
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 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 →
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 →
Automated Quantification of Tumor-Infiltrating Lymphocytes
Developing an algorithmn that can automatically detect and segment tumor-infiltrating lymphocytes in breast cancer.
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 →
VPH-PRISM
Study on the correlations between several radiological modalities and histpathology for more understanding of breast cancer presentation.
Read more →