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Updated: Jun 13, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Q-Seg:基于量子制的不受监督的图像分割.

Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske

IEEE computer graphics and applications

|September 9, 2024

概括

此摘要是机器生成的。

我们开发了Q-Seg,这是一种新的无监督图像分割技术,在当前的量子硬件上使用量子化. 这种方法在速度和可扩展性方面卓越,用于像地球观测这样的复杂任务,即使数据有限.

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相关实验视频

Last Updated: Jun 13, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

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Published on: July 5, 2024

科学领域:

量子计算是一种量子计算.
计算机视觉计算机视觉
图像细分图像细分

背景情况:

无监督的图像细分对于分析复杂的视觉数据至关重要.
现有的量子和经典方法在可扩展性和性能方面面临挑战.
地球观测数据往往遭受噪音和不可靠的注释.

研究的目的:

介绍Q-Seg,一种新的无监督图像细分方法.
杆量子化用于高效的图形切割优化.
评估Q-Seg的性能与经典方法和先进技术相比.

主要方法:

制定像素化图像分割作为图形切割优化问题.
使用D-Wave优势量子回火架构.使用D-Wave优势量子回火架构.
吸收光谱和空间图像信息.

主要成果:

与现有的量子方法相比,Q-Seg显示出更高的可扩展性.
在经验评估中优于最先进的古典方法.
在合成数据集上表现出比古典优化器Gurobi更好的运行时间性能.
成功应用于地球观测图像分割任务.

结论:

使用现有的量子硬件,Q-Seg提供了一个有前途的无监督图像细分解决方案.
为现实世界应用提供可靠的替代方案,特别是在有限的标记数据的情况下.
有效地与高级技术竞争,如任何细分,特别是在杂的环境中.