Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification Article Swipe
Wenjing Shuai
,
Jianzhao Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics11091510
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics11091510
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics11091510
- https://www.mdpi.com/2079-9292/11/9/1510/pdf?version=1652078448
- OA Status
- gold
- Cited By
- 10
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4229446653
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4229446653Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics11091510Digital Object Identifier
- Title
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Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-05-09Full publication date if available
- Authors
-
Wenjing Shuai, Jianzhao LiList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics11091510Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-9292/11/9/1510/pdf?version=1652078448Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/11/9/1510/pdf?version=1652078448Direct OA link when available
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Computer science, Artificial intelligence, Exploit, Coding (social sciences), Benchmark (surveying), Deep learning, Key (lock), Convolutional neural network, Pixel, Machine learning, Pattern recognition (psychology), Computer security, Geography, Mathematics, Geodesy, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1, 2023: 4, 2022: 3Per-year citation counts (last 5 years)
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46Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2047243114, https://openalex.org/W3131096279, https://openalex.org/W3187018989, https://openalex.org/W2070813603, https://openalex.org/W1964069486, https://openalex.org/W4214929894, https://openalex.org/W3114674012, https://openalex.org/W2998460251, https://openalex.org/W3160565231, https://openalex.org/W3213283212, https://openalex.org/W4213126894, https://openalex.org/W2295990357, https://openalex.org/W2800292742, https://openalex.org/W2806321514, https://openalex.org/W2054648089, https://openalex.org/W3024371423, https://openalex.org/W3095500215, https://openalex.org/W2804233018, https://openalex.org/W2964050365, https://openalex.org/W2909358189, https://openalex.org/W1850407572, https://openalex.org/W1508301986, https://openalex.org/W3035682985, https://openalex.org/W2980274421, https://openalex.org/W6756519181, https://openalex.org/W6717697761, https://openalex.org/W6735236233, https://openalex.org/W6756861123, https://openalex.org/W2604763608, https://openalex.org/W6743661861, https://openalex.org/W3113410735, https://openalex.org/W3137988398, https://openalex.org/W4285604868, https://openalex.org/W2752782242, https://openalex.org/W3095948197, https://openalex.org/W2794825826, https://openalex.org/W2914568698, https://openalex.org/W2912564039, https://openalex.org/W2194775991, https://openalex.org/W2964105864, https://openalex.org/W2895671740, https://openalex.org/W3164330620, https://openalex.org/W2904218366, https://openalex.org/W3098394437, https://openalex.org/W2904008038, https://openalex.org/W3102785203 |
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| corresponding_author_ids | https://openalex.org/A5067729205 |
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