Work packages:
WP 1: Data collection, patient inclusion and prespecification of the
experiments
WP 2: Training and testing of the neural networks with image data
WP 3: Training and testing of a multimodal neural network
WP 4: Comparing the models
WP 5: Reverse engineering of the best performing model
Project recruiting
Our research proposal pursues four specific objectives:
1.) The development of deep learning methods for diagnosis of PSVD based on histological
image data, including multicentre blinded validation.
2.) Combination of image-based diagnosis prediction with clinical data to develop a multimodal
deep learning system.
3.) Identification of features that are recognized as predictive for PSVD
4.) Finding novel clusters and previously unknown biological subgroups of PSVD based on
reverse engineering approaches.
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