SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Doan, Tan N. N. | - |
dc.contributor.author | Song, Boram | - |
dc.contributor.author | Vuong, Trinh T. L. | - |
dc.contributor.author | Kim, Kyungeun | - |
dc.contributor.author | Kwak, Jin T. | - |
dc.date.accessioned | 2022-08-12T18:40:31Z | - |
dc.date.available | 2022-08-12T18:40:31Z | - |
dc.date.created | 2022-08-12 | - |
dc.date.issued | 2022-07 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/142936 | - |
dc.description.abstract | Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of different types of nuclei. Herein, we propose a self-guided ordinal regression neural network for simultaneous nuclear segmentation and classification that can exploit the intrinsic characteristics of nuclei and focus on highly uncertain areas during training. The proposed network formulates nuclei segmentation as an ordinal regression learning by introducing a distance decreasing discretization strategy, which stratifies nuclei in a way that inner regions forming a regular shape of nuclei are separated from outer regions forming an irregular shape. It also adopts a self-guided training strategy to adaptively adjust the weights associated with nuclear pixels, depending on the difficulty of the pixels that is assessed by the network itself. To evaluate the performance of the proposed network, we employ large-scale multi-tissue datasets with 276349 exhaustively annotated nuclei. We show that the proposed network achieves the state-of-the-art performance in both nuclei segmentation and classification in comparison to several methods that are recently developed for segmentation and/or classification. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | OBJECTS | - |
dc.title | SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kwak, Jin T. | - |
dc.identifier.doi | 10.1109/JBHI.2022.3149936 | - |
dc.identifier.scopusid | 2-s2.0-85124769723 | - |
dc.identifier.wosid | 000819832600037 | - |
dc.identifier.bibliographicCitation | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.26, no.7, pp.3218 - 3228 | - |
dc.relation.isPartOf | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.citation.title | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.citation.volume | 26 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 3218 | - |
dc.citation.endPage | 3228 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.subject.keywordPlus | OBJECTS | - |
dc.subject.keywordAuthor | Image segmentation | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Shape | - |
dc.subject.keywordAuthor | Histopathology | - |
dc.subject.keywordAuthor | Decoding | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Digital pathology | - |
dc.subject.keywordAuthor | nuclei classification | - |
dc.subject.keywordAuthor | nuclei segmentation | - |
dc.subject.keywordAuthor | ordinal regression | - |
dc.subject.keywordAuthor | self-guided learning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.