Titlebook: Soil Dynamics, Earthquake and Computational Geotechnical Engineering; Proceedings of the I Kasinathan Muthukkumaran,R. Ayothiraman,Sreevals

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S. Vijayakumar,M. Muttharamlving an ill-posed dipole inversion problem. Conventional single orientation QSM methods usually employ regularization strategies to stabilize such inversion, but may suffer from streaking artifacts or over-smoothing. Multiple orientation QSM such as calculation of susceptibility through multiple or

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Aritra Bagchi,Prishati Raychowdhury images, it is often impractical to build a large homogeneous annotated dataset for a specific pathology. Self-supervised methods offer a new way to learn a representation of the images in an unsupervised manner with a neural network. In particular, contrastive learning has shown great promises by (

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Piyush Katariya,Mayur G. Vanza annotations, which are very expensive and require huge human resources. In this work, we adopt a coarse-to-fine strategy and propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. Specifically, we design a dual-task network, including a shared enco

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Kritesh Chouhan,Jitesh T. Chavdaly learn a nonlinear voxel-wise mapping function between volumetric images by metric space alignments in the spatial domain, without addressing the intrinsic structure correspondence. Thus, the registration requires prior affine transformation or landmark annotations to handle high-frequency perturb

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Parul Rawat,Supriya Mohantynference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.*.The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in

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