MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention 
This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.
This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?
Med Image Comput Comput Assist Interv. 2023 Oct;14224:663-673. doi: 10.1007/978-3-031-43904-9_64. Epub 2023 Oct 1.ABSTRACTPruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient we...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - October 13, 2023 Category: Radiology Authors: Gregory Holste Ziyu Jiang Ajay Jaiswal Maria Hanna Shlomo Minkowitz Alan C Legasto Joanna G Escalon Sharon Steinberger Mark Bittman Thomas C Shen Ying Ding Ronald M Summers George Shih Yifan Peng Zhangyang Wang Source Type: research
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration
Med Image Comput Comput Assist Interv. 2022 Sep;13436:66-77. doi: 10.1007/978-3-031-16446-0_7. Epub 2022 Sep 17.ABSTRACTEstablishing voxelwise semantic correspondence across distinct imaging modalities is a foundational yet formidable computer vision task. Current multi-modality registration techniques maximize hand-crafted inter-domain similarity functions, are limited in modeling nonlinear intensity-relationships and deformations, and may require significant re-engineering or underperform on new tasks, datasets, and domain pairs. This work presents ContraReg, an unsupervised contrastive representation learning approach t...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - August 14, 2023 Category: Radiology Authors: Neel Dey Jo Schlemper Seyed Sadegh Mohseni Salehi Bo Zhou Guido Gerig Michal Sofka Source Type: research
