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An Accurate Neural Network for Cytologic Whole-Slide Image Analysis

Authors:

Jing KeAuthors Info & Claims

ACSW '20: Proceedings of the Australasian Computer Science Week Multiconference

Article No.: 22, Pages 1 - 7

https://doi.org/10.1145/3373017.3373039

Published: 04 February 2020 Publication History

Abstract

Typically, high accuracy in deep learning is achieved by large dataset in pixel-wise labeling for segmentation or image-level labeling for classification. However, in biomedical domain, the challenge is not only the availability of image data itself, but also the acquisition of relevant annotations for these images from clinicians. In this work, we propose a novel two-stage architecture to jointly perform the tasks of detection, segmentation and classification of abnormal cells and cancer. Compared with one-step detection for all the catalogues, we combine the advantage of image-level and pixel-level labeling in our deep learning based framework. We use the detection of lesions in cervical clinical dataset as a case study for performance evaluation. In the first stage, a hybrid ResNet and U-Net architecture is designed to predict three catalogues of nuclei, cytoplasm and background with pixel-wise labeled segmentation map. In the second stage, a residual learning based model is applied to the identified nuclei for subtype classification. Confirmed with cytotechnologist, the proposed model is estimated to efficiently deduct more than 90% annotation burden compared with pixel-wise labeling approach. Moreover, the proposed two-stage approach model outperforms one-stage neural network in segmentation and classification for objects with high similarities in appearance. Our collected real-life clinical cytology images and the source code in the experiments are provided in https://github.com/SJTU-AI-GPU/TwoStageCellSegmentation.1

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Cited By

Pang WQiu YJin SJiang HMa Y(2025)Label credibility correction based on cell morphological differences for cervical cells classificationScientific Reports10.1038/s41598-024-84899-815:1Online publication date: 2-Jan-2025
https://doi.org/10.1038/s41598-024-84899-8
Pang WMa YJiang HYu Q(2024)Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology ImagesBioengineering10.3390/bioengineering1201002312:1(23)Online publication date: 30-Dec-2024
https://doi.org/10.3390/bioengineering12010023
Sambyal DSarwar A(2023)Recent developments in cervical cancer diagnosis using deep learning on whole slide images: An Overview of models, techniques, challenges and future directionsMicron10.1016/j.micron.2023.103520173(103520)Online publication date: Oct-2023
https://doi.org/10.1016/j.micron.2023.103520
Show More Cited By

An Accurate Neural Network for Cytologic Whole-Slide Image Analysis
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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ACSW '20: Proceedings of the Australasian Computer Science Week Multiconference

February 2020

367 pages

ISBN:9781450376976

DOI:10.1145/3373017

Copyright © 2020 ACM.

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Publication History

Published: 04 February 2020

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ACSW '20

ACSW '20: Australasian Computer Science Week 2020

February 4 - 6, 2020

VIC, Melbourne, Australia

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Overall Acceptance Rate 61 of 141 submissions, 43%

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Cited By

Pang WQiu YJin SJiang HMa Y(2025)Label credibility correction based on cell morphological differences for cervical cells classificationScientific Reports10.1038/s41598-024-84899-815:1Online publication date: 2-Jan-2025
https://doi.org/10.1038/s41598-024-84899-8
Pang WMa YJiang HYu Q(2024)Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology ImagesBioengineering10.3390/bioengineering1201002312:1(23)Online publication date: 30-Dec-2024
https://doi.org/10.3390/bioengineering12010023
Sambyal DSarwar A(2023)Recent developments in cervical cancer diagnosis using deep learning on whole slide images: An Overview of models, techniques, challenges and future directionsMicron10.1016/j.micron.2023.103520173(103520)Online publication date: Oct-2023
https://doi.org/10.1016/j.micron.2023.103520
K HVetriselvi V(2022)Deep Learning based Classification of Cervical Cancer using Transfer Learning2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC)10.1109/ICESIC53714.2022.9783560(134-139)Online publication date: 22-Apr-2022
https://doi.org/10.1109/ICESIC53714.2022.9783560
Matias ACerentini AMacarini LAmorim JDaltoé Fvon Wangenheim A(2021)Segmentation, Detection, and Classification of Cell Nuclei on Oral Cytology Samples Stained with PapanicolaouSN Computer Science10.1007/s42979-021-00676-82:4Online publication date: 21-May-2021
https://doi.org/10.1007/s42979-021-00676-8

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