Abstract: Widening the Focus

The field of biomedical computer vision has considerably been influenced by image analysis challenges, which are mainly dominated by deep learning-based approaches. Much effort is put into challenge-specific optimization of model design, training schemes and data augmentation techniques. The paper [1] aims to widen the focus beyond model architecture and training pipeline design by shedding a light on inference efficiency and the role of patch sampling strategies for large images that cannot be processed at once.