  This paper studies the problem of detecting and segmenting acute intracranial
hemorrhage on head computed tomography (CT) scans. We propose to solve both
tasks as a semantic segmentation problem using a patch-based fully
convolutional network (PatchFCN). This formulation allows us to accurately
localize hemorrhages while bypassing the complexity of object detection. Our
system demonstrates competitive performance with a human expert and the
state-of-the-art on classification tasks (0.976, 0.966 AUC of ROC on
retrospective and prospective test sets) and on segmentation tasks (0.785 pixel
AP, 0.766 Dice score), while using much less data and a simpler system. In
addition, we conduct a series of controlled experiments to understand "why"
PatchFCN outperforms standard FCN. Our studies show that PatchFCN finds a good
trade-off between batch diversity and the amount of context during training.
These findings may also apply to other medical segmentation tasks.
