More from less: Overcoming Data Scarcity for Deep Learning in Medical Image Computing

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This project aims at closing the gap between the performance of AI models in classic computer vision and natural language processing and the application of machine learning methods to radiological images by leveraging existing data more effectively. Specifically, we formulate the following goals:1. Simulate tomographic medical images using Generative Adversarial Networks (GANs) more effectively by leveraging all spatial context in 3D. We will then answer the question to what degree the synthesized data is beneficial as additional training data for classification problems. This will reveal how much real data is needed in order to train a GAN on medical images in a stable manner. Furthermore, this will provide evidence for the question to what the degree the performance of a classifier may be improved by artificial images.2. Develop models for inductive transfer learning which may be used to train algorithms for other related problems with much less data. The models and weights will be released as open source software. This will show under which conditions transfer learning is superior to training from scratch.3. Translate the success of self-supervised learning from natural language processing (NLP) into medical image computing (MIC) problems by developing problem-specific pretext tasks and loss functions. This will utilize existing data much more efficiently than in tranditional supervised learning.4. Develop probabilistic segmentation algorithms that model the distribution of possible tumor segmentations in 3D. This will provide even more precise probabilistic segmentations and provides a basis for many downstream tasks such as radiomics.