Multi-Magnification Attention Convolutional Neural Networks [AI-eXplained]

被引：2

作者：

Chao, Chia-Wei ^{[1
]}

Hwang, Daniel Winden ^{[1
]}

Tsai, Hung-Wen ^{[1
]}

Lin, Shih-Hsuan ^{[1
]}

Chen, Wei-Li ^{[1
]}

Huang, Chun-Rong ^{[1
]}

Chung, Pau-Choo ^{[1
]}

机构：

[1] Natl Cheng Kung Univ, Tainan, Taiwan

来源：

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | 2023年 / 18卷 / 03期

关键词：

Pathology; Image segmentation; Image analysis; Liver; Convolutional neural networks;

D O I：

10.1109/MCI.2023.3277771

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To apply convolutional neural networks (CNNs) on high-resolution images, a common approach is to split the input image into smaller patches. However, the field-of-view is restricted by the input size. To overcome the problem, a multi-magnification attention convolutional neural network (MMA-CNN) is proposed to analyze images based on both local and global features. Our approach focuses on identifying the importance of individual features at each magnification level and is applied to pathology whole slide images (WSIs) segmentation to show its effectiveness. Several interactive figures are also developed to enhance the reader's understanding of our research.

引用

页码：54 / 55

页数：2

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