Multimodal Extension of the ML-CSC Framework for Medical Image Segmentation
In recent years, Convolutional Neural Networks (CNNs) have led to huge successes across various computer vision applications. However, the lack of interpretability poses a severe barrier for their wider adoption in healthcare. Recently introduced Multilayer Convolutional Sparse Coding (ML-CSC) data...
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| Published in: | 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA) pp. 91 - 96 |
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| Main Authors: | , , , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
13.09.2021
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| ISSN: | 1849-2266 |
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| Abstract | In recent years, Convolutional Neural Networks (CNNs) have led to huge successes across various computer vision applications. However, the lack of interpretability poses a severe barrier for their wider adoption in healthcare. Recently introduced Multilayer Convolutional Sparse Coding (ML-CSC) data model provides a model-based explanation of CNNs. This article aims to extend the ML-CSC framework towards multimodal data processing, which to our knowledge has not been addressed so far. In particular, we focus on interpretable medical image segmentation architecture design for multimodal data. We derive a novel sparse coding algorithm and propose three different CNN architectures with increasing performance, without introducing any additional learnable parameters. Based on the sparse coding theory, our multimodal extension enables the systematic design of interpretable CNN segmentation architectures. Experimental analysis demonstrates that the achieved segmentation results are consistent with the obtained theoretical expectations. |
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| AbstractList | In recent years, Convolutional Neural Networks (CNNs) have led to huge successes across various computer vision applications. However, the lack of interpretability poses a severe barrier for their wider adoption in healthcare. Recently introduced Multilayer Convolutional Sparse Coding (ML-CSC) data model provides a model-based explanation of CNNs. This article aims to extend the ML-CSC framework towards multimodal data processing, which to our knowledge has not been addressed so far. In particular, we focus on interpretable medical image segmentation architecture design for multimodal data. We derive a novel sparse coding algorithm and propose three different CNN architectures with increasing performance, without introducing any additional learnable parameters. Based on the sparse coding theory, our multimodal extension enables the systematic design of interpretable CNN segmentation architectures. Experimental analysis demonstrates that the achieved segmentation results are consistent with the obtained theoretical expectations. |
| Author | Huang, Shaoguang Lazendic, Srdan Pizurica, Aleksandra Janssens, Jens |
| Author_xml | – sequence: 1 givenname: Jens surname: Janssens fullname: Janssens, Jens email: Jens.Janssens@UGent.be organization: GAIM,Department of Telecommunications and Information Processing – sequence: 2 givenname: Srdan surname: Lazendic fullname: Lazendic, Srdan email: Srdan.Lazendic@UGent.be organization: Ghent University,Clifford Research Group, Faculty of Engineering and Architecture,Department of Electronics and Information Systems,Belgium – sequence: 3 givenname: Shaoguang surname: Huang fullname: Huang, Shaoguang email: Shaoguang.Huang@UGent.be organization: GAIM,Department of Telecommunications and Information Processing – sequence: 4 givenname: Aleksandra surname: Pizurica fullname: Pizurica, Aleksandra email: Aleksandra.Pizurica@UGent.be organization: GAIM,Department of Telecommunications and Information Processing |
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| Snippet | In recent years, Convolutional Neural Networks (CNNs) have led to huge successes across various computer vision applications. However, the lack of... |
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| SubjectTerms | Computer architecture Convolution Convolutional codes Encoding Image segmentation interpretable convolutional neural networks medical image segmentation Multilayer convolutional sparse coding multimodal data Nonhomogeneous media Signal processing algorithms |
| Title | Multimodal Extension of the ML-CSC Framework for Medical Image Segmentation |
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