基于深度概率规划的超声神经分割

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of ICT Research and Applications Pub Date : 2019-12-31 DOI:10.5614/itbj.ict.res.appl.2019.13.3.5
Iresha D. Rubasinghe, D. Meedeniya
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引用次数: 16

摘要

深度概率编程将深度学习的优势与概率建模相结合,在实践中实现高效灵活的计算。作为一个不断发展的领域,用于不确定性管理的表达性编程语言屈指可数。本文讨论了超声神经分割在生物医学图像分析中的应用。我们的方法使用概率编程语言Edward和U-Net模型,并在不同的优化器下生成对抗性网络。与其他优化器相比,U-Net模型中的Adam优化器的分割过程显示出最小的骰子损失(’0.54)和最高的精度(0.99),时间消耗最少。Adam优化器在生成对抗性网络模型中获得的生成网络损失最小为0.69。结果中的骰子损失、精度、时间消耗和输出图像质量表明了深度概率规划在长期中的适用性。因此,我们进一步提出了一个基于所提出方法的神经科学决策支持系统。
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Ultrasound Nerve Segmentation Using Deep Probabilistic Programming
Deep probabilistic programming concatenates the strengths of deep learning to the context of probabilistic modeling for efficient and flexible computation in practice. Being an evolving field, there exist only a few expressive programming languages for uncertainty management. This paper discusses an application for analysis of ultrasound nerve segmentation-based biomedical images. Our method uses the probabilistic programming language Edward with the U-Net model and generative adversarial networks under different optimizers. The segmentation process showed the least Dice loss (‑0.54) and the highest accuracy (0.99) with the Adam optimizer in the U-Net model with the least time consumption compared to other optimizers. The smallest amount of generative network loss in the generative adversarial network model gained was 0.69 for the Adam optimizer. The Dice loss, accuracy, time consumption and output image quality in the results show the applicability of deep probabilistic programming in the long run. Thus, we further propose a neuroscience decision support system based on the proposed approach.
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来源期刊
Journal of ICT Research and Applications
Journal of ICT Research and Applications COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
1.60
自引率
0.00%
发文量
13
审稿时长
24 weeks
期刊介绍: Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management. Authors are invited to submit articles that have not been published previously and are not under consideration elsewhere.
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