COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation

Wooseok Shin, Min Seok Lee, Sung Won Han

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)

    Abstract

    Colonoscopy is an effective method for detecting polyps to prevent colon cancer. Existing studies have achieved satisfactory polyp detection performance by aggregating low-level boundary and high-level region information in convolutional neural networks (CNNs) for precise polyp segmentation in colonoscopy images. However, multi-level aggregation provides limited polyp segmentation owing to the distribution discrepancy that occurs when integrating different layer representations. To address this problem, previous studies have employed complementary low-and high-level represen-tations. In contrast to existing methods, we focus on propagating complementary information such that the complementary low-level explicit boundary with abstracted high-level representations dimin-ishes the discrepancy. This study proposes COMMA, which propagates complementary multi-level aggregation to reduce distribution discrepancies. COMMA comprises a complementary masking module (CMM) and a boundary propagation module (BPM) as a multi-decoder. The CMM masks the low-level boundary noises through the abstracted high-level representation and leverages the masked information at both levels. Similarly, the BPM incorporates the lowest-and highest-level representations to obtain explicit boundary information and propagates the boundary to the CMMs to improve polyp detection. CMMs can discriminate polyps more elaborately than prior CMMs based on boundary and complementary representations. Moreover, we propose a hybrid loss function to mitigate class imbalance and noisy annotations in polyp segmentation. To evaluate the COMMA performance, we conducted experiments on five benchmark datasets using five metrics. The results proved that the proposed network outperforms state-of-the-art methods in terms of all datasets. Specifically, COMMA improved mIoU performance by 0.043 on average for all datasets compared to the existing state-of-the-art methods.

    Original languageEnglish
    Article number2114
    JournalApplied Sciences (Switzerland)
    Volume12
    Issue number4
    DOIs
    Publication statusPublished - 2022 Feb 1

    Bibliographical note

    Funding Information:
    Funding: This research was supported by Brain Korea 21 FOUR. This research was also supported by National Research Foundation of Korea (NRF-2019R1F1A1060250).

    Publisher Copyright:
    © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

    Keywords

    • Colonoscopy
    • Colorectal cancer
    • Convolutional neural network
    • Deep learning
    • Polyp segmentation

    ASJC Scopus subject areas

    • General Materials Science
    • Instrumentation
    • General Engineering
    • Process Chemistry and Technology
    • Computer Science Applications
    • Fluid Flow and Transfer Processes

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