Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability
The Algorithm selection approach improves performance by dynamically choosing the optimal Algorithm for each input instance. While this selection strategy has been extensively studied, the amount of data and their nature have not yet been investigated with respect to meta-learning, particularly in s...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/6/310 |
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| author | Maxim Zhabinets Benjamin Tyler Martin Lukac Shinobu Nagayama Ferdinand Molnár Michitaka Kameyama |
| author_facet | Maxim Zhabinets Benjamin Tyler Martin Lukac Shinobu Nagayama Ferdinand Molnár Michitaka Kameyama |
| author_sort | Maxim Zhabinets |
| collection | DOAJ |
| description | The Algorithm selection approach improves performance by dynamically choosing the optimal Algorithm for each input instance. While this selection strategy has been extensively studied, the amount of data and their nature have not yet been investigated with respect to meta-learning, particularly in scenarios with limited data availability. This paper addresses a critical challenge: where additional data might not be available for training an Algorithm selector, and to implement a selection mechanism, data must be generated. Focusing on medical image classification, we investigate whether synthetic data can effectively train an Algorithm selector when real training data are scarce. Our methodology involves data generation using Generative Adversarial Network. To determine if Algorithm selection trained on synthetically generated data can achieve the same accuracy as if trained on real-world natural data, we systematically evaluate the data generative model using the smallest amount of data needed to choose the right Algorithm and to achieve the expected level of accuracy. Our experimental results demonstrate that using a small amount of real samples can provide enough information to a Generative Adversarial Network to synthesize a new dataset that, when used for training the Algorithm selection, improves image classification in some cases. |
| format | Article |
| id | doaj-art-391e3f61bc9d472ca3b39b22cc8379fa |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-391e3f61bc9d472ca3b39b22cc8379fa2025-08-20T03:26:24ZengMDPI AGAlgorithms1999-48932025-05-0118631010.3390/a18060310Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data AvailabilityMaxim Zhabinets0Benjamin Tyler1Martin Lukac2Shinobu Nagayama3Ferdinand Molnár4Michitaka Kameyama5School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, KazakhstanSchool of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, KazakhstanGraduate School of Information Sciences, Hiroshima City University, Hiroshima 731-3166, JapanGraduate School of Information Sciences, Hiroshima City University, Hiroshima 731-3166, JapanSchool of Sciences and Humanities, Nazarbayev University, Astana 010000, KazakhstanEmeritus of Graduate School of Information Sciences, Tohoku University, Sendai 980-8577, JapanThe Algorithm selection approach improves performance by dynamically choosing the optimal Algorithm for each input instance. While this selection strategy has been extensively studied, the amount of data and their nature have not yet been investigated with respect to meta-learning, particularly in scenarios with limited data availability. This paper addresses a critical challenge: where additional data might not be available for training an Algorithm selector, and to implement a selection mechanism, data must be generated. Focusing on medical image classification, we investigate whether synthetic data can effectively train an Algorithm selector when real training data are scarce. Our methodology involves data generation using Generative Adversarial Network. To determine if Algorithm selection trained on synthetically generated data can achieve the same accuracy as if trained on real-world natural data, we systematically evaluate the data generative model using the smallest amount of data needed to choose the right Algorithm and to achieve the expected level of accuracy. Our experimental results demonstrate that using a small amount of real samples can provide enough information to a Generative Adversarial Network to synthesize a new dataset that, when used for training the Algorithm selection, improves image classification in some cases.https://www.mdpi.com/1999-4893/18/6/310algorithm selectionmedical image classificationsynthetic dataGAN |
| spellingShingle | Maxim Zhabinets Benjamin Tyler Martin Lukac Shinobu Nagayama Ferdinand Molnár Michitaka Kameyama Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability Algorithms algorithm selection medical image classification synthetic data GAN |
| title | Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability |
| title_full | Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability |
| title_fullStr | Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability |
| title_full_unstemmed | Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability |
| title_short | Synthetic Data-Based Algorithm Selection for Medical Image Classification Under Limited Data Availability |
| title_sort | synthetic data based algorithm selection for medical image classification under limited data availability |
| topic | algorithm selection medical image classification synthetic data GAN |
| url | https://www.mdpi.com/1999-4893/18/6/310 |
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