Benefits of Mirror Weight Symmetry for 3D Mesh Segmentation in Biomedical Applications
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Benefits of Mirror Weight Symmetry for 3D Mesh Segmentation in Biomedical Applications
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| Автор |
Dordiuk, V.
Dzhigil, M. Ushenin, K. |
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| Тематика |
3D MESH SEGMENTATION
BIOMEDICAL SEGMENTATION HARD CONSTRAINTS INVERSION INVARIANT ROTATION INVARIANT SYMMETRY IN NEURAL NETWORKS WEIGHT SYMMETRY CONVOLUTION CONVOLUTIONAL NEURAL NETWORKS HEART MEDICAL APPLICATIONS MULTILAYER NEURAL NETWORKS 3D MESH SEGMENTATION 3D MESHES BIOMEDICAL SEGMENTATION HARD CONSTRAINTS INVERSION INVARIANT MESH SEGMENTATION NEURAL-NETWORKS ROTATION INVARIANT SYMMETRY IN NEURAL NETWORK WEIGHT SYMMETRY MESH GENERATION |
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| Описание |
3D mesh segmentation is an important task with many biomedical applications. The human body has bilateral symmetry and some variations in organ positions. It allows us to expect a positive effect of rotation and inversion invariant layers in convolutional neural networks that perform biomedical segmentations. In this study, we show the impact of weight symmetry in neural networks that perform 3D mesh segmentation. We analyze the problem of 3D mesh segmentation for pathological vessel structures (aneurysms) and conventional anatomical structures (endocardium and epicardium of ventricles). Local geometrical features are encoded as sampling from the signed distance function, and the neural network performs prediction for each mesh node. We show that weight symmetry gains from 1 to 3% of additional accuracy and allows decreasing the number of trainable parameters up to 8 times without suffering the performance loss if neural networks have at least three convolutional layers. This also works for very small training sets. © 2023 IEEE.
Russian Science Foundation, RSF: RSF 22-21-00930 This work has been supported by the grant of the Russian Science Foundation, RSF 22-21-00930. The computations were performed on the Uran supercomputer at the IMM UB RAS. |
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| Дата |
2024-04-05T16:38:14Z
2024-04-05T16:38:14Z 2023 |
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| Тип |
Conference Paper
Conference object (info:eu-repo/semantics/conferenceObject) info:eu-repo/semantics/submittedVersion |
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| Идентификатор |
Dordiuk, V, Dzhigil, M & Ushenin, K 2023, Benefits of Mirror Weight Symmetry for 3D Mesh Segmentation in Biomedical Applications. в 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023 - Proceedings: book. Institute of Electrical and Electronics Engineers Inc., стр. 100-107, 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB), 28/09/2023. https://doi.org/10.1109/CSGB60362.2023.10329838
Dordiuk, V., Dzhigil, M., & Ushenin, K. (2023). Benefits of Mirror Weight Symmetry for 3D Mesh Segmentation in Biomedical Applications. в 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023 - Proceedings: book (стр. 100-107). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSGB60362.2023.10329838 9798350307979 Final All Open Access, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180377048&doi=10.1109%2fCSGB60362.2023.10329838&partnerID=40&md5=66ad56216256930400f994c8fa9007e0 https://arxiv.org/pdf/2309.17076 http://elar.urfu.ru/handle/10995/131073 10.1109/CSGB60362.2023.10329838 85180377048 |
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| Язык |
en
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| Связанные ресурсы |
info:eu-repo/grantAgreement/RSF//22-21-00930
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| Права |
Open access (info:eu-repo/semantics/openAccess)
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| Формат |
application/pdf
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| Издатель |
Institute of Electrical and Electronics Engineers Inc.
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| Источник |
2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB)
2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023 - Proceedings |
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