Dual-feature Fusion Attention Network for Small Object Segmentation

Accurate segmentation of medical images is an important step during radiotherapy planning and clinical diagnosis. However, manually marking organ or lesion boundaries is tedious, time-consuming, and prone to error due to subjective variability of radiologist. Automatic segmentation remains a challen...

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Veröffentlicht in:	Computers in biology and medicine Jg. 160; S. 106985
Hauptverfasser:	Fei, Xin, Li, Xiaojie, Shi, Canghong, Ren, Hongping, Mumtaz, Imran, Guo, Jun, Wu, Yu, luo, Yong, Lv, Jiancheng, Wu, Xi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.06.2023 Elsevier Limited
Schlagworte:	Accuracy Artificial neural networks Boundaries Complementarity Dual-branch feature fusion Humans Image enhancement Image processing Image Processing, Computer-Assisted Image segmentation Internal Medicine Medical imaging Modules Multi-resolution features Neural networks Neural Networks, Computer Other Polyps Radiation therapy Radiologists Reverse attention Small object segmentation Small object segmentation Dual-branch feature fusion Multi-resolution features Reverse attention
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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