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.

本文研究如何利用 Patch-based Fully Convolutional Network 方法对头部 CT 扫描图像进行语义分割，以准确识别和定位颅内急性出血，进而提高分类和分割性能，同时试验结果表明该方法在医学图像分割任务中具有较好的普适性。