Weakly supervised deep learning-based intracranial hemorrhage localization

2021/05/03 by Nemcek, Jakub, Vicar, Tomas, Jakubicek, Roman
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Medical Physics (physics.med-ph)

paper · doi:10.48550/arxiv.2105.00781

Abstract

Intracranial hemorrhage is a life-threatening disease, which requires fast medical intervention. Owing to the duration of data annotation, head CT images are usually available only with slice-level labeling. This paper presents a weakly supervised method of precise hemorrhage localization in axial slices using only position-free labels, which is based on multiple instance learning. An algorithm is introduced that generates hemorrhage likelihood maps and finds the coordinates of bleeding. The Dice coefficient of 58.08 % is achieved on data from a publicly available dataset.

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