Diseases are classi ed as multifocal if they are relating to or arising from many foci. They are present in various
medical disciplines, e.g. multifocal atrial tachycardia [1], breast cancer [2] or multifocal motor neuropathy [3].
However, analyzing diseases with multiple centers brings several challenges for conventional deep learning ar-
chitectures. On a technical side, it is complex to handle a varying number of centers which have no unique
sequence. From a medical view, it is important to model structures and relationships between the foci. The grid
structure used in convolutional neural networks cannot handle non-regular neighborhoods. A suitable approach
for this task would be to convert the data into graph structures, where the nodes describe the properties of the
foci and the edges model their mutual relationships. With geometric deep learning, it is possible to learn from
graph structures. It is an emerging eld of research with many possible applications, e.g. classifying documents
in citation graphs or analyzing molecular structures [4]. There also exist several medical applications, e.g. for
analysis of parcinson’s disease [5] or artery segmentation [6]. This thesis aims to investigate the applicability of
this method for relatively small graphs coming from multifocal diseases. The networks are trained to predict
time to events of failure as a metric for the severeness of the disease. Di erent geometric layer architectures,
such as Graph-Attention-Networks [7] and Di erential Pooling [8], are investigated and compared to the per-
formance of a conventional neural network. As we aim to create explicable models, it is intended to provide
visualizations of salient sub-graphs and features of the results. In addition to that, methods to incorporate prior
knowledge from the medical domain into the training process are tested to improve the speed of convergence
and strengthen the medical validity of the predictions. In the end, the networks are tested on liver data.
Summary:
1. Transfer multifocal diseases to meaningful graph structures
2. Provide conventional neural network for time to event regression as baseline
3. Investigate and tune di erent geometric deep learning architectures
4. Visualize salient graph structures
References
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Article in Journal of Clinical Oncology, 23:7497{7502, 2005.
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62{69. Springer, oct 2019.
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