COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
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 colon...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/4/2114 |
| _version_ | 1797483023604645888 |
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| author | Wooseok Shin Min Seok Lee Sung Won Han |
| author_facet | Wooseok Shin Min Seok Lee Sung Won Han |
| author_sort | Wooseok Shin |
| collection | DOAJ |
| description | 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 representations. In contrast to existing methods, we focus on propagating complementary information such that the complementary low-level explicit boundary with abstracted high-level representations diminishes 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. |
| first_indexed | 2024-03-09T22:40:57Z |
| format | Article |
| id | doaj.art-c3767591f824450abb54798eb465a111 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:40:57Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c3767591f824450abb54798eb465a1112023-11-23T18:39:30ZengMDPI AGApplied Sciences2076-34172022-02-01124211410.3390/app12042114COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp SegmentationWooseok Shin0Min Seok Lee1Sung Won Han2School of Industrial and Management Engineering, Korea University, 145, Anam-Ro, Seongbuk-Gu, Seoul 02841, KoreaSchool of Industrial and Management Engineering, Korea University, 145, Anam-Ro, Seongbuk-Gu, Seoul 02841, KoreaSchool of Industrial and Management Engineering, Korea University, 145, Anam-Ro, Seongbuk-Gu, Seoul 02841, KoreaColonoscopy 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 representations. In contrast to existing methods, we focus on propagating complementary information such that the complementary low-level explicit boundary with abstracted high-level representations diminishes 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.https://www.mdpi.com/2076-3417/12/4/2114colorectal cancercolonoscopypolyp segmentationdeep learningconvolutional neural network |
| spellingShingle | Wooseok Shin Min Seok Lee Sung Won Han COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation Applied Sciences colorectal cancer colonoscopy polyp segmentation deep learning convolutional neural network |
| title | COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation |
| title_full | COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation |
| title_fullStr | COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation |
| title_full_unstemmed | COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation |
| title_short | COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation |
| title_sort | comma propagating complementary multi level aggregation network for polyp segmentation |
| topic | colorectal cancer colonoscopy polyp segmentation deep learning convolutional neural network |
| url | https://www.mdpi.com/2076-3417/12/4/2114 |
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