no code implementations • CVPR 2023 • Hyungseob Shin, Hyeongyu Kim, Sewon Kim, Yohan Jun, Taejoon Eo, Dosik Hwang
Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner.
no code implementations • 30 Mar 2022 • Hyungseob Shin, Hyeongyu Kim, Sewon Kim, Yohan Jun, Taejoon Eo, Dosik Hwang
In this work, we propose a self-training based unsupervised domain adaptation framework for 3D medical image segmentation named COSMOS and validate it with automatic segmentation of Vestibular Schwannoma (VS) and cochlea on high-resolution T2 Magnetic Resonance Images (MRI).
no code implementations • 22 Sep 2021 • Hyungseob Shin, Hyeongyu Kim, Sewon Kim, Yohan Jun, Taejoon Eo, Dosik Hwang
With the advances of deep learning, many medical image segmentation studies achieve human-level performance when in fully supervised condition.
no code implementations • CVPR 2021 • Yohan Jun, Hyungseob Shin, Taejoon Eo, Dosik Hwang
Joint-ICNet has two main blocks, where one is an MR image reconstruction block that reconstructs an MR image from undersampled multi-coil k-space data and the other is a coil sensitivity maps reconstruction block that estimates coil sensitivity maps from undersampled multi-coil k-space data.
1 code implementation • CVPR 2021 • Jeong Ryong Lee, Sewon Kim, Inyong Park, Taejoon Eo, Dosik Hwang
A common explanation method is Class Activation Mapping(CAM) based method where it is often used to understand the last layer of the convolutional neural networks popular in the field of Computer Vision.