Machine Learning and the Life Sciences
Artificial Intelligence has become pivotal for research and innovation in health care. An increasing number of algorithms find their way to clinical practice providing powerful solutions and assisting medical doctors in their everyday practice. We study deep-learning-based approaches in this domain and work towards novel, unbiased, and generalizable algorithms for cancer treatment and response to immunotherapy. Of particular interest are learning schemes for training on gigapixel histopathological slides, transformer-based architectures with different attention schemes for the fusion of histopathology and genetic/clinical information, and bias identification and domain adaptation methods based on the image-to-image translation and adversarial attacks for addressing domain shifts and possibly biological and clinical biases.