ColonNeXt: Fully Convolutional Attention for Polyp Segmentation.
This study introduces ColonNeXt, a novel fully convolutional attention-based model for polyp segmentation from colonoscopy images, aimed at the enhancing early detection of colorectal cancer. Utilizing a purely convolutional neural network (CNN), ColonNeXt integrates an encoder-decoder structure wit...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2194 - 2210 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278978&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278978 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278978 187278978 187278978 10.1007/s10278-024-01342-0 187278978 ppf: 2194 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: ColonNeXt: Fully Convolutional Attention for Polyp Segmentation. aug: au: Nguyen, Dinh Cong Nguyen, Hoang Long affil: https://ror.org/05dp8mg49 Hong Duc University, 565 Quang Trung, Dong Ve Ward, 40000, Thanh Hoa, Thanh Hoa, Viet Nam sug: subj: Image Processing, Computer Assisted Convolutional Neural Networks Utilization Colonoscopy Colonic Polyps Early Detection of Cancer Methods Colorectal Neoplasms Diagnosis Diagnosis, Computer Assisted Human ROC Curve Descriptive Statistics ab: This study introduces ColonNeXt, a novel fully convolutional attention-based model for polyp segmentation from colonoscopy images, aimed at the enhancing early detection of colorectal cancer. Utilizing a purely convolutional neural network (CNN), ColonNeXt integrates an encoder-decoder structure with a hierarchical multi-scale context-aware network (MSCAN) in the encoder and a convolutional block attention module (CBAM) in the decoder. The decoder further includes a proposed CNN-based feature attention mechanism for selective feature enhancement, ensuring precise segmentation. A new refinement module effectively improves boundary accuracy, addressing challenges such as variable polyp size, complex textures, and inconsistent illumination. Evaluations on standard datasets show that ColonNeXt achieves high accuracy and efficiency, significantly outperforming competing methods. These results confirm its robustness and precision, establishing ColonNeXt as a state-of-the-art model for polyp segmentation. The code is available at: https://github.com/long-nguyen12/colonnext-pytorch. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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