Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI
Segmentation neural networks are widely used in medical imaging to identify anomalies that may impact patient health. Despite their effectiveness, these networks face significant challenges, including the need for extensive annotated patient data, time-consuming manual segmentation processes and res...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/2/55 |
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| author | Arturs Nikulins Edgars Edelmers Kaspars Sudars Inese Polaka |
| author_facet | Arturs Nikulins Edgars Edelmers Kaspars Sudars Inese Polaka |
| author_sort | Arturs Nikulins |
| collection | DOAJ |
| description | Segmentation neural networks are widely used in medical imaging to identify anomalies that may impact patient health. Despite their effectiveness, these networks face significant challenges, including the need for extensive annotated patient data, time-consuming manual segmentation processes and restricted data access due to privacy concerns. In contrast, classification neural networks, similar to segmentation neural networks, capture essential parameters for identifying objects during training. This paper leverages this characteristic, combined with explainable artificial intelligence (XAI) techniques, to address the challenges of segmentation. By adapting classification neural networks for segmentation tasks, the proposed approach reduces dependency on manual segmentation. To demonstrate this concept, the Medical Segmentation Decathlon ‘Brain Tumours’ dataset was utilised. A ResNet classification neural network was trained, and XAI tools were applied to generate segmentation-like outputs. Our findings reveal that GuidedBackprop is among the most efficient and effective methods, producing heatmaps that closely resemble segmentation masks by accurately highlighting the entirety of the target object. |
| format | Article |
| id | doaj-art-ffee84e99d7f4962bff7ebc677914e8e |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-ffee84e99d7f4962bff7ebc677914e8e2025-08-20T03:12:22ZengMDPI AGJournal of Imaging2313-433X2025-02-011125510.3390/jimaging11020055Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AIArturs Nikulins0Edgars Edelmers1Kaspars Sudars2Inese Polaka3Faculty of Computer Science, Information Technology and Energy, Riga Technical University, LV-1048 Riga, LatviaFaculty of Computer Science, Information Technology and Energy, Riga Technical University, LV-1048 Riga, LatviaInstitute of Electronics and Computer Science, LV-1006 Riga, LatviaFaculty of Computer Science, Information Technology and Energy, Riga Technical University, LV-1048 Riga, LatviaSegmentation neural networks are widely used in medical imaging to identify anomalies that may impact patient health. Despite their effectiveness, these networks face significant challenges, including the need for extensive annotated patient data, time-consuming manual segmentation processes and restricted data access due to privacy concerns. In contrast, classification neural networks, similar to segmentation neural networks, capture essential parameters for identifying objects during training. This paper leverages this characteristic, combined with explainable artificial intelligence (XAI) techniques, to address the challenges of segmentation. By adapting classification neural networks for segmentation tasks, the proposed approach reduces dependency on manual segmentation. To demonstrate this concept, the Medical Segmentation Decathlon ‘Brain Tumours’ dataset was utilised. A ResNet classification neural network was trained, and XAI tools were applied to generate segmentation-like outputs. Our findings reveal that GuidedBackprop is among the most efficient and effective methods, producing heatmaps that closely resemble segmentation masks by accurately highlighting the entirety of the target object.https://www.mdpi.com/2313-433X/11/2/55medical imagingclassification modelsimage segmentationexplainable artificial intelligenceneural networks |
| spellingShingle | Arturs Nikulins Edgars Edelmers Kaspars Sudars Inese Polaka Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI Journal of Imaging medical imaging classification models image segmentation explainable artificial intelligence neural networks |
| title | Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI |
| title_full | Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI |
| title_fullStr | Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI |
| title_full_unstemmed | Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI |
| title_short | Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI |
| title_sort | adapting classification neural network architectures for medical image segmentation using explainable ai |
| topic | medical imaging classification models image segmentation explainable artificial intelligence neural networks |
| url | https://www.mdpi.com/2313-433X/11/2/55 |
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