Weakly supervised deep learning-based intracranial hemorrhage localization

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.

Localization and classification of intracranialhemorrhages in CT data

Intracranial hemorrhages (ICHs) are life-threatening brain injures with a relatively high incidence. In this paper, the automatic algorithm for the detection and classification of ICHs, including localization, is present. The set of binary convolutional neural network-based classifiers with a designed cascade-parallel architecture is used. This automatic system may lead to a distinct decrease in the diagnostic process's duration in acute cases. An average Jaccard coefficient of 53.7 % is achieved on the data from the publicly available head CT dataset CQ500.

A Tool for Automatic Estimation of Patient Position in Spinal CT Data

Much of the recently available research and challenge data lack the meta-data containing any information about the patient position. This paper presents a tool for automatic rotation of CT data into a standardized (HFS) patient position. The proposed method is based on the prediction of rotation angle with CNN, and it achieved nearly perfect results with an accuracy of 99.55 %. We provide implementations with easy to use an example for both Matlab and Python (PyTorch), which can be used, for example, for automatic rotation correction of VerSe2020 challenge data.