{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T20:23:37Z","timestamp":1776803017877,"version":"3.51.2"},"reference-count":33,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T00:00:00Z","timestamp":1708300800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T00:00:00Z","timestamp":1708300800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology (KIAT)","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,19]]},"DOI":"10.1109\/icaiic60209.2024.10463270","type":"proceedings-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T18:12:10Z","timestamp":1710958330000},"page":"1-5","source":"Crossref","is-referenced-by-count":3,"title":["Enhanced Kidney Tumor Segmentation in CT Scans Using a Simplified UNETR with Organ Information"],"prefix":"10.1109","author":[{"given":"Sanghyuk Roy","family":"Choi","sequence":"first","affiliation":[{"name":"Chung-Ang University,Department of Intelligent Semiconductor Engineering,Seoul,Korea"}]},{"given":"Kanghyeok","family":"Ko","sequence":"additional","affiliation":[{"name":"Chung-Ang University,Department of Intelligent Semiconductor Engineering,Seoul,Korea"}]},{"given":"Sun Jae","family":"Baek","sequence":"additional","affiliation":[{"name":"Chung-Ang University,Department of Intelligent Semiconductor Engineering,Seoul,Korea"}]},{"given":"Soyeon","family":"Lee","sequence":"additional","affiliation":[{"name":"Chung-Ang University,Department of Intelligent Semiconductor Engineering,Seoul,Korea"}]},{"given":"Jungro","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronics Engineering, Chung-Ang University,Seoul,Korea"}]},{"given":"Minhyeok","family":"Lee","sequence":"additional","affiliation":[{"name":"Chung-Ang University,Department of Intelligent Semiconductor Engineering,Seoul,Korea"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.10.013"},{"key":"ref2","first-page":"1","volume-title":"Understanding of a convolutional neural network\u2019, in Editor (Ed.)^(Eds.): \u2018Book Understanding of a convolutional neural network","author":"Albawi","year":"2017"},{"key":"ref3","first-page":"844","volume-title":"Medical image classification with convolutional neural network\u2019, in Editor (Ed.)^(Eds.): \u2018Book Medical image classification with convolutional neural network","author":"Li","year":"2014"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1511.08458"},{"key":"ref5","first-page":"234","volume-title":"U-net: Convolutional networks for biomedical image segmentation\u2019, in Editor (Ed.)^(Eds.): \u2018Book U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"ref6","first-page":"3","volume-title":"Unet++: A nested u-net architecture for medical image segmentation\u2019, in Editor (Ed.)^(Eds.): \u2018Book Unet++: A nested unet architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3130469"},{"key":"ref8","article-title":"Deepvit: Towards deeper vision transformer","author":"Zhou","year":"2021","journal-title":"arXiv preprint"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3505244"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3268446"},{"key":"ref11","article-title":"\u2018Vit-v-net: Vision transformer for unsupervised volumetric medical image registration","author":"Chen","year":"2021","journal-title":"arXiv preprint"},{"key":"ref12","article-title":"\u2018Vision transformer for smallsize datasets","author":"Lee","year":"2021","journal-title":"arXiv preprint"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00745"},{"key":"ref14","first-page":"147","volume-title":"SESR: Single image super resolution with recursive squeeze and excitation networks\u2019, in Editor (Ed.)^(Eds.): \u2018Book SESR: Single image super resolution with recursive squeeze and excitation networks","author":"Cheng","year":"2018"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2019.1462"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/rs11151817"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/biology11101462"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/biology11040586"},{"key":"ref19","article-title":"Ammus: A survey of transformer-based pretrained models in natural language processing","author":"Kalyan","year":"2021","journal-title":"arXiv preprint"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"ref21","article-title":"An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv preprint"},{"key":"ref22","first-page":"2286","volume-title":"Convit: Improving vision transformers with soft convolutional inductive biases\u2019, in Editor (Ed.)^(Eds.): \u2018Book Convit: Improving vision transformers with soft convolutional inductive biases\u2019","author":"dAscoli","year":"2021"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"ref24","first-page":"1213","volume-title":"DBT-UNETR: Double Branch Transformer with Cross Fusion for 3D Medical Image Segmentation\u2019, in Editor (Ed.)^(Eds.): \u2018Book DBT-UNETR: Double Branch Transformer with Cross Fusion for 3D Medical Image Segmentation","author":"Tao","year":"2022"},{"key":"ref25","first-page":"114","volume-title":"\u2018Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET\/CT Imaging: A Transfer Learning Approach\u2019: \u20183D Head and Neck Tumor Segmentation in PET\/CT Challenge","author":"Chu","year":"2022"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.236"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.555"},{"key":"ref28","article-title":"Uncertainty quantified deep learning for predicting dice coefficient of digital histopathology image segmentation","author":"Ghosal","year":"2021","journal-title":"arXiv preprint"},{"key":"ref29","first-page":"92","volume-title":"Optimizing the dice score and jaccard index for medical image segmentation: Theory and practice\u2019, in Editor (Ed.)^(Eds.): \u2018Book Optimizing the dice score and jaccard index for medical image segmentation: Theory and practice","author":"Bertels","year":"2019"},{"key":"ref30","first-page":"240","volume-title":"Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations\u2019, in Editor (Ed.)^(Eds.): \u2018Book Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations","author":"Sudre","year":"2017"},{"key":"ref31","article-title":"\u2018Decoupled weight decay regularization","author":"Loshchilov","year":"2017","journal-title":"arXiv preprint"},{"key":"ref32","first-page":"4165","volume-title":"Research on data augmentation for image classification based on convolution neural networks\u2019, in Editor (Ed.)^(Eds.): \u2018Book Research on data augmentation for image classification based on convolution neural networks","author":"Shijie","year":"2017"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"}],"event":{"name":"2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","location":"Osaka, Japan","start":{"date-parts":[[2024,2,19]]},"end":{"date-parts":[[2024,2,22]]}},"container-title":["2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10463165\/10463194\/10463270.pdf?arnumber=10463270","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T19:35:06Z","timestamp":1711481706000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10463270\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,19]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/icaiic60209.2024.10463270","relation":{},"subject":[],"published":{"date-parts":[[2024,2,19]]}}}