Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916589679411200 |
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| author | Bryan IV, J Shepard Pessoa, Pedro Tavakoli, Meyam Presse, Steve |
| author_facet | Bryan IV, J Shepard Pessoa, Pedro Tavakoli, Meyam Presse, Steve |
| contents | Deep learning based approaches are now widely used across biophysics to help automate a variety of tasks including image segmentation, feature selection, and deconvolution. However, the presence of multiple competing deep learning architectures, each with its own unique advantages and disadvantages, makes it challenging to select an architecture best suited for a specific application. As such, we present a comprehensive comparison of common models. Here, we focus on the task of segmentation assuming the typically small training dataset sizes available from biophysics experiments and compare the following four commonly used architectures: convolutional neural networks, U-Nets, vision transformers, and vision state space models. In doing so, we establish criteria for determining optimal conditions under which each model excels, thereby offering practical guidelines for researchers and practitioners in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07786 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data Bryan IV, J Shepard Pessoa, Pedro Tavakoli, Meyam Presse, Steve Image and Video Processing Computer Vision and Pattern Recognition Biological Physics Deep learning based approaches are now widely used across biophysics to help automate a variety of tasks including image segmentation, feature selection, and deconvolution. However, the presence of multiple competing deep learning architectures, each with its own unique advantages and disadvantages, makes it challenging to select an architecture best suited for a specific application. As such, we present a comprehensive comparison of common models. Here, we focus on the task of segmentation assuming the typically small training dataset sizes available from biophysics experiments and compare the following four commonly used architectures: convolutional neural networks, U-Nets, vision transformers, and vision state space models. In doing so, we establish criteria for determining optimal conditions under which each model excels, thereby offering practical guidelines for researchers and practitioners in the field. |
| title | Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Biological Physics |
| url | https://arxiv.org/abs/2408.07786 |