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Curriculum Incremental Deep Learning on BreakHis DataSet

Published: 20 September 2022 Publication History

Abstract

This paper examines methodological aspects of the training procedure of neural networks for medical image classification. The research question concerns the conjecture that: feeding a network with datasets of increasing magnification leverages high-level knowledge and helps the network to better classify. This study confirms this hypothesis by an experiment carried out on a dataset of breast cancer histopathological images. Results are presented that underline the importance of the order in which data is introduced to the neural network during the training phase. Extensive experiments done on the BreakHis dataset demonstrate that curriculum incremental learning reaches 98.76% accuracy for binary classification while the best state of the art approach only reaches 96.78.%. Concerning multi-class classification, curriculum incremental learning reaches 95.93% while the state of the art approaches only reaches 95.49%. Moreover both the computational time and the stabilization time of the learning process of the incremental curriculum learning approach are reduced (respectively by 6% and by more than 20%) wrt a non curriculum learning approach.

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  • (2024)Improving Breast Tumor Multi-Classification from High-Resolution Histological Images with the Integration of Feature Space Data AugmentationInformation10.3390/info1502009815:2(98)Online publication date: 8-Feb-2024
  • (2024)Zoom is Meaningful: Discerning Ultrasound Images’ Zoom Levels2024 IEEE International Symposium on Biomedical Imaging (ISBI)10.1109/ISBI56570.2024.10635854(1-5)Online publication date: 27-May-2024
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  1. Curriculum Incremental Deep Learning on BreakHis DataSet

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    cover image ACM Other conferences
    ICCTA '22: Proceedings of the 2022 8th International Conference on Computer Technology Applications
    May 2022
    286 pages
    ISBN:9781450396226
    DOI:10.1145/3543712
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 20 September 2022

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    Author Tags

    1. breast cancer
    2. deep learning
    3. histopathological images
    4. learning process.

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    View all
    • (2024)Improving Breast Tumor Multi-Classification from High-Resolution Histological Images with the Integration of Feature Space Data AugmentationInformation10.3390/info1502009815:2(98)Online publication date: 8-Feb-2024
    • (2024)Zoom is Meaningful: Discerning Ultrasound Images’ Zoom Levels2024 IEEE International Symposium on Biomedical Imaging (ISBI)10.1109/ISBI56570.2024.10635854(1-5)Online publication date: 27-May-2024
    • (2024)Zoom Pattern Signatures for Fetal Ultrasound StructuresMedical Image Computing and Computer Assisted Intervention – MICCAI 202410.1007/978-3-031-72083-3_73(786-795)Online publication date: 14-Oct-2024
    • (2022)Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological ImagesDiagnostics10.3390/diagnostics1301010313:1(103)Online publication date: 29-Dec-2022

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