Projects & Software
Current research projects
![](https://assets.diagnijmegen.nl/images/projects/bigpicture.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/deep-derma-logo.png)
Deep Derma
The aim of this project is to apply artificial intelligence to detect basal cell carcinoma.
Read more →![](https://assets.diagnijmegen.nl/images/projects/Prostate3_2_1.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/ignite.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/lung-cancer-early-stage-treatment.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/pathrad_fusion.jpg)
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 →![](https://assets.diagnijmegen.nl/images/projects/STIC.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/TB2-1.jpg)
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 →![](https://assets.diagnijmegen.nl/images/projects/unesco.png)
UNESCO
The aim of UNESCO is to study techniques for estimating uncertainty in computational pathology.
Read more →![](https://assets.diagnijmegen.nl/images/projects/unic.png)
UNIC
The aim of UNIC is to develop artificial intelligence methods to refine diffuse-type gastric cancer (DGC) diagnostics.
Read more →Software
![](https://assets.diagnijmegen.nl/images/software/asap_screenshot.png)
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.
![](https://assets.diagnijmegen.nl/images/software/automated_gleason_grading.png)
Automated Gleason Grading
Automated analysis of prostate biopsies following the Gleason grading system.
![](https://assets.diagnijmegen.nl/images/software/concept_hooknet2.png)
HookNet
HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
![](https://assets.diagnijmegen.nl/images/software/neural_image_compression.png)
Neural Image Compression
Neural image compression for gigapixel histopathology image analysis.
Finished projects
![](https://assets.diagnijmegen.nl/images/projects/ami-combined.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/Aquila_2_1.png)
AQUILA
The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.
Read more →![](https://assets.diagnijmegen.nl/images/vacancies/pathology_artifact_detection_msc.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/camelyon17_2_1.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/DCIS_2_1.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/ExaMode.png)
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 →![](https://assets.diagnijmegen.nl/images/vacancies/msc_patho_breast_risk_predict.jpg)
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 →![](https://assets.diagnijmegen.nl/images/projects/Multimot.png)
Multimot
MULTIMOT aims to build an open data ecosystem for cell migration research, through standardization, dissemination and meta-analysis efforts.
Read more →![](https://assets.diagnijmegen.nl/images/projects/panda_logo_1_2.png)
PANDA Challenge
Challenge on prostate cancer grading of biopsies using the Gleason grading system.
Read more →![](https://assets.diagnijmegen.nl/images/projects/PRIMB4C_1.jpg)
PRIM4BC
Detecting biomarkers for improved prognosis for triple negative breast cancer by combining histopathology, multiplex immunohistochemistry and Deep Learning.
Read more →![](https://assets.diagnijmegen.nl/images/projects/proacting.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/logo_STITPRO.png)
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 →![](https://assets.diagnijmegen.nl/images/projects/asap.png)
STITPRO II
Project with focus on the extension of open-source software ASAP that includes functionality for study and case management.
Read more →![](https://assets.diagnijmegen.nl/images/projects/SM_kidney2-1.jpg)
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 →![](https://assets.diagnijmegen.nl/images/projects/nucls.jpg)
Automated Quantification of Tumor-Infiltrating Lymphocytes
Developing an algorithmn that can automatically detect and segment tumor-infiltrating lymphocytes in breast cancer.
Read more →![](https://assets.diagnijmegen.nl/images/projects/TSR.png)
Tumor-Stroma Ratio
We aim at developing automatic and reproducible quantification of Tumor-Stroma Ratio in Whole-Slide Images using Deep Learning.
Read more →![](https://assets.diagnijmegen.nl/images/projects/logo_vphprism.jpg)
VPH-PRISM
Study on the correlations between several radiological modalities and histpathology for more understanding of breast cancer presentation.
Read more →