PatchFCN for Intracranial Hemorrhage Detection

This paper studies the problem of detecting acute intracranial hemorrhage on head computed tomography (CT) scans. We formulate it as a pixel-wise labeling task of the frames that constitute a single head scan. The standard approach for this task is the fully convolutional network (FCN) which runs on the whole image at both training and test time. We propose a patch-based approach that controls the amount of context available to the FCN, based on the observation that when radiologists are interpreting CT scans, their judgment depends primarily on local cues and does not require the whole image context. To develop and validate the system, we collected a pixel-wise labeled dataset of 591 scans that covers a wide range of hemorrhage types and imaging conditions in the real world. We show that no pretraining from natural images is needed. By aggregating the pixel-wise labeling, our system is able to make region-level, frame-level, and stack-level decisions. Our final system approaches an expert radiologist performance with a high average precision (AP) of 96.5 +/- 1.3 % for hemorrhage classification on stack level while running at 23ms per frame