PaperView - going over the paper -> "Windsor, R., Jamaludin, A., Kadir, T. and Zisserman, A., 2021. Self-supervised multi-modal alignment for whole body medical imaging. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021, 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II 24 (pp. 90-101). Springer International Publishing."
PaperView - going over the paper titled "Mask scoring r-cnn" by Zhaojin Huang, Lichao Huang, Yongchao Gong, Chang Huang, Xinggang Wang
Intracranial hemorrhage occurs when blood vessels rupture or leak within the brain tissue or elsewhere inside the skull. It can be caused by physical trauma or by various medical conditions and in many cases leads to death. The treatment must be started as soon as possible, and therefore the hemorrhage should be diagnosed accurately and quickly. The diagnosis is usually performed by a radiologist who analyses a Computed Tomography (CT) scan containing a large number of cross-sectional images throughout the brain. Analysing each image manually can be very time-consuming, but automated techniques can help speed up the process. While much of the recent research has focused on solving this problem by using supervised machine learning algorithms, publicly-available training data remains scarce due to privacy concerns. This problem can be alleviated by unsupervised algorithms. In this paper, we propose a fully-unsupervised algorithm which is based on the mixture models. Our algorithm utilizes the fact that the properties of hemorrhage and healthy tissues follow different distributions, and therefore an appropriate formulation of these distributions allows us to separate them through an Expectation-Maximization process. In addition, our algorithm is able to adaptively determine the number of clusters such that all the hemorrhage regions can be found without including noisy voxels. We demonstrate the results of our algorithm on publicly-available datasets that contain all different hemorrhage types in various sizes and intensities, and our results are compared to earlier unsupervised and supervised algorithms. The results show that our algorithm can outperform the other algorithms with most hemorrhage types.
PaperView - going over the paper titled "Panoptic segmentation with a joint semantic and instance segmentation network" by De Geus, Daan, Panagiotis Meletis, and Gijs Dubbelman.