Computer Vision News - October 2022

55 MICCAI Workshop: STACOM The second challenge ( CMRxMotion ) was organized by yet another participant from Fudan University, Shuo Wang and hosted by Chen Chen and Ouyang Cheng from Imperial College. It addressed respiratory motion artefacts with two main tasks: 1) CMR image quality assessment and 2) robust CMR segmentation. This session included talks on a wide range of techniques- from recurrent neural networks and “insane” data augmentation to deformable convolutions, multi-task learning and ensemble classification frameworks. Yasmina Al Khalil started introducing the method OPENGTN, which won 3rd Place on both tasks, and included two sections, an auto-encoder trained to reconstruct images with noise for prediction of quality control, and then an ensemble of models to improve robustness through data augmentation helped by region-based training which segmented apical, middle, and basal slices separately. The 2nd place on Task 1 was won by the Philips CTS method from Xiuzheng Yue , combining deep learning for global view and machine learning for LV radiomics feature extraction through voting, while the 1st place was achieved by UON_IMA, from Ruizhe LI , where the author used a biased voting strategy to aggregate the decisions from different patch-based models. EST OF MICCAI