Multiscale and Hierarchical Feature-Aggregation Network for Segmenting Medical Images

We propose an encoder–decoder architecture using wide and deep convolutional layers combined with different aggregation modules for the segmentation of medical images. Initially, we obtain a rich representation of features that span from low to high levels and from small to large scales by stacking...

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Bibliographic Details
Main Authors:	Choi, J.Y (Author), Lee, B. (Author), Yamanakkanavar, N. (Author)
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	Aggregation network Convolution convolutional neural network Convolutional neural network Decoding Encoder-decoder architecture Feature aggregation feature fusion Features fusions Hierarchical features Image segmentation Iterative methods Low-to-high Medical image segmentation Medical imaging medical-image segmentation Multi-scale features Network layers Segmentation of medical images Signal encoding
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Multiscale and Hierarchical Feature-Aggregation Network for Segmenting Medical Images

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