Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification
Humans are born with the ability to learn quickly by discerning objects from a few samples, to acquire new skills in a short period of time, and to make decisions based on limited prior experience and knowledge. The existing deep learning models for medical image classification often rely on a large...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/9/1510 |
| _version_ | 1797505101566312448 |
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| author | Wenjing Shuai Jianzhao Li |
| author_facet | Wenjing Shuai Jianzhao Li |
| author_sort | Wenjing Shuai |
| collection | DOAJ |
| description | Humans are born with the ability to learn quickly by discerning objects from a few samples, to acquire new skills in a short period of time, and to make decisions based on limited prior experience and knowledge. The existing deep learning models for medical image classification often rely on a large number of labeled training samples, whereas the fast learning ability of deep neural networks has failed to develop. In addition, it requires a large amount of time and computing resource to retrain the model when the deep model encounters classes it has never seen before. However, for healthcare applications, enabling a model to generalize new clinical scenarios is of great importance. The existing image classification methods cannot explicitly use the location information of the pixel, making them insensitive to cues related only to the location. Besides, they also rely on local convolution and cannot properly utilize global information, which is essential for image classification. To alleviate these problems, we propose a collateral location coding to help the network explicitly exploit the location information of each pixel to make it easier for the network to recognize cues related to location only, and a single-key global spatial attention is designed to make the pixels at each location perceive the global spatial information in a low-cost way. Experimental results on three medical image benchmark datasets demonstrate that our proposed algorithm outperforms the state-of-the-art approaches in both effectiveness and generalization ability. |
| first_indexed | 2024-03-10T04:13:48Z |
| format | Article |
| id | doaj.art-24253d304c1e423184f1654f500d71c3 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T04:13:48Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-24253d304c1e423184f1654f500d71c32023-11-23T08:04:36ZengMDPI AGElectronics2079-92922022-05-01119151010.3390/electronics11091510Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image ClassificationWenjing Shuai0Jianzhao Li1School of Electronic Engineering, Xidian University, Xi’an 710071, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Electronic Engineering, Xidian University, Xi’an 710071, ChinaHumans are born with the ability to learn quickly by discerning objects from a few samples, to acquire new skills in a short period of time, and to make decisions based on limited prior experience and knowledge. The existing deep learning models for medical image classification often rely on a large number of labeled training samples, whereas the fast learning ability of deep neural networks has failed to develop. In addition, it requires a large amount of time and computing resource to retrain the model when the deep model encounters classes it has never seen before. However, for healthcare applications, enabling a model to generalize new clinical scenarios is of great importance. The existing image classification methods cannot explicitly use the location information of the pixel, making them insensitive to cues related only to the location. Besides, they also rely on local convolution and cannot properly utilize global information, which is essential for image classification. To alleviate these problems, we propose a collateral location coding to help the network explicitly exploit the location information of each pixel to make it easier for the network to recognize cues related to location only, and a single-key global spatial attention is designed to make the pixels at each location perceive the global spatial information in a low-cost way. Experimental results on three medical image benchmark datasets demonstrate that our proposed algorithm outperforms the state-of-the-art approaches in both effectiveness and generalization ability.https://www.mdpi.com/2079-9292/11/9/1510few-shot learningcomputational intelligencemedical image classificationspatial attention |
| spellingShingle | Wenjing Shuai Jianzhao Li Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification Electronics few-shot learning computational intelligence medical image classification spatial attention |
| title | Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification |
| title_full | Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification |
| title_fullStr | Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification |
| title_full_unstemmed | Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification |
| title_short | Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification |
| title_sort | few shot learning with collateral location coding and single key global spatial attention for medical image classification |
| topic | few-shot learning computational intelligence medical image classification spatial attention |
| url | https://www.mdpi.com/2079-9292/11/9/1510 |
| work_keys_str_mv | AT wenjingshuai fewshotlearningwithcollaterallocationcodingandsinglekeyglobalspatialattentionformedicalimageclassification AT jianzhaoli fewshotlearningwithcollaterallocationcodingandsinglekeyglobalspatialattentionformedicalimageclassification |