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SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images

Authors
Doan, Tan N. N.Song, BoramVuong, Trinh T. L.Kim, KyungeunKwak, Jin T.
Issue Date
7월-2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Image segmentation; Training; Shape; Histopathology; Decoding; Convolutional neural networks; Task analysis; Digital pathology; nuclei classification; nuclei segmentation; ordinal regression; self-guided learning
Citation
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.26, no.7, pp.3218 - 3228
Indexed
SCIE
SCOPUS
Journal Title
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume
26
Number
7
Start Page
3218
End Page
3228
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/142936
DOI
10.1109/JBHI.2022.3149936
ISSN
2168-2194
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.
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공과대학 (전기전자공학부)
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