  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.
