Data augmentation based on multiple oversampling fusion for medical image segmentation

A high-performance medical image segmentation model based on deep learning depends on the availability of large amounts of annotated training data. However, it is not trivial to obtain sufficient annotated medical images. Generally, the small size of most tissue lesions, e.g., pulmonary nodules and...

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Veröffentlicht in:	PloS one 2022-10, Vol.17 (10), p.e0274522
Hauptverfasser:	Wu, Liangsheng, Zhuang, Jiajun, Chen, Weizhao, Tang, Yu, Hou, Chaojun, Li, Chentong, Zhong, Zhenyu, Luo, Shaoming
Format:	Artikel
Sprache:	eng
Schlagworte:	Abdomen Accuracy Affine transformations Analysis Annotations Artificial neural networks Biology and Life Sciences Cancer Classification Computed tomography Computer and Information Sciences CT imaging Data augmentation Datasets Deep learning Diagnosis Entropy Evaluation Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation Lesions Liver Liver cancer Liver Neoplasms Lung cancer Lung nodules Machine learning Medical imaging Medicine and Health Sciences Methods Modelling Multidimensional data Multiple Pulmonary Nodules Neural networks Neural Networks, Computer Nodules Oversampling Performance evaluation Pixels Research and Analysis Methods Tissues Training Tumors
Online-Zugang:	Volltext
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