Medical image classification by incorporating clinical variables and learned features
Medical image classification plays an important role in medical imaging. In this work, we present a novel approach to enhance deep learning models in medical image classification by incorporating clinical variables without overwhelming the information. Unlike most existing deep neural network models...
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| Format: | Article |
| Language: | English |
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The Royal Society
2025-03-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.241222 |
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| _version_ | 1850041503413436416 |
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| author | Jiahui Liu Xiaohao Cai Mahesan Niranjan |
| author_facet | Jiahui Liu Xiaohao Cai Mahesan Niranjan |
| author_sort | Jiahui Liu |
| collection | DOAJ |
| description | Medical image classification plays an important role in medical imaging. In this work, we present a novel approach to enhance deep learning models in medical image classification by incorporating clinical variables without overwhelming the information. Unlike most existing deep neural network models that only consider single-pixel information, our method captures a more comprehensive view. Our method contains two main steps and is effective in tackling the extra challenge raised by the scarcity of medical data. Firstly, we employ a pre-trained deep neural network served as a feature extractor to capture meaningful image features. Then, an exquisite discriminant analysis is applied to reduce the dimensionality of these features, ensuring that the low number of features remains optimized for the classification task and striking a balance with the clinical variables information. We also develop a way of obtaining class activation maps for our approach in visualizing models’ focus on specific regions within the low-dimensional feature space. Thorough experimental results demonstrate improvements of our proposed method over state-of-the-art methods for tuberculosis and dermatology issues for example. Furthermore, a comprehensive comparison with a popular dimensionality reduction technique (principal component analysis) is also conducted. |
| format | Article |
| id | doaj-art-8254dd712efd4129947a4a67ef4bf577 |
| institution | DOAJ |
| issn | 2054-5703 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-8254dd712efd4129947a4a67ef4bf5772025-08-20T02:55:45ZengThe Royal SocietyRoyal Society Open Science2054-57032025-03-0112310.1098/rsos.241222Medical image classification by incorporating clinical variables and learned featuresJiahui Liu0Xiaohao Cai1Mahesan Niranjan2School of Electronics and Computer Science, University of Southampton, Southampton, UKSchool of Electronics and Computer Science, University of Southampton, Southampton, UKSchool of Electronics and Computer Science, University of Southampton, Southampton, UKMedical image classification plays an important role in medical imaging. In this work, we present a novel approach to enhance deep learning models in medical image classification by incorporating clinical variables without overwhelming the information. Unlike most existing deep neural network models that only consider single-pixel information, our method captures a more comprehensive view. Our method contains two main steps and is effective in tackling the extra challenge raised by the scarcity of medical data. Firstly, we employ a pre-trained deep neural network served as a feature extractor to capture meaningful image features. Then, an exquisite discriminant analysis is applied to reduce the dimensionality of these features, ensuring that the low number of features remains optimized for the classification task and striking a balance with the clinical variables information. We also develop a way of obtaining class activation maps for our approach in visualizing models’ focus on specific regions within the low-dimensional feature space. Thorough experimental results demonstrate improvements of our proposed method over state-of-the-art methods for tuberculosis and dermatology issues for example. Furthermore, a comprehensive comparison with a popular dimensionality reduction technique (principal component analysis) is also conducted.https://royalsocietypublishing.org/doi/10.1098/rsos.241222medical imagingclassificationdiscriminant analysisclinical variablesclass activation map |
| spellingShingle | Jiahui Liu Xiaohao Cai Mahesan Niranjan Medical image classification by incorporating clinical variables and learned features Royal Society Open Science medical imaging classification discriminant analysis clinical variables class activation map |
| title | Medical image classification by incorporating clinical variables and learned features |
| title_full | Medical image classification by incorporating clinical variables and learned features |
| title_fullStr | Medical image classification by incorporating clinical variables and learned features |
| title_full_unstemmed | Medical image classification by incorporating clinical variables and learned features |
| title_short | Medical image classification by incorporating clinical variables and learned features |
| title_sort | medical image classification by incorporating clinical variables and learned features |
| topic | medical imaging classification discriminant analysis clinical variables class activation map |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.241222 |
| work_keys_str_mv | AT jiahuiliu medicalimageclassificationbyincorporatingclinicalvariablesandlearnedfeatures AT xiaohaocai medicalimageclassificationbyincorporatingclinicalvariablesandlearnedfeatures AT mahesanniranjan medicalimageclassificationbyincorporatingclinicalvariablesandlearnedfeatures |