Polyp Segmentation With the FCB-SwinV2 Transformer

Polyp segmentation within colonoscopy video frames using deep learning models has the potential to automate colonoscopy screening procedures. This could help improve the early lesion detection rate and in vivo characterization of polyps which could develop into colorectal cancer. Recent state-of-the...

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Veröffentlicht in:	IEEE access 2024-01, Vol.12, p.38927-38943
Hauptverfasser:	Fitzgerald, Kerr, Bernal, Jorge, Histace, Aymeric, Matuszewski, Bogdan J.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial Intelligence Artificial neural networks Biomedical image processing Colonoscopy Colorectal cancer Computer Science Computer Vision and Pattern Recognition Convolutional neural networks Datasets Decoding Deep learning Feature extraction Image segmentation In vivo methods and tests Machine learning Medical image processing polyp segmentation Polyps Segmentation SwinV2 Training transformer Transformers Tumors
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