在反向问题的任务驱动的不确定性量化通过规范预测.
Jeffrey Wen1, Rizwan Ahmad1, Philip Schniter1
1The Ohio State University, Columbus OH 43210, USA.
Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision
|May 29, 2025
概括
本研究引入了一种以任务为中心的方法,用于从不完整的数据中量化图像恢复中的不确定性. 符合性预测保证了准确的任务输出预测,使得适应性数据采集能够用于改进的成像应用,如加速MRI.
科学领域:
- 医疗成像医学成像
- 计算成像技术的成像
- 不确定性定量化 不确定性定量化
背景情况:
- 从不完整或损坏的测量中重建图像是一个错误的问题.
- 在恢复图像中的量化不确定性对于下游应用,如分类,至关重要.
- 现有的方法往往缺乏特定任务的不确定性量化.
研究的目的:
- 开发一个以任务为中心的不确定性量化方法用于图像重建.
- 为了保证从重建图像中获得的任务输出的准确性.
- 根据任务不确定性水平,以自适应的方式获取测量结果.
主要方法:
- 利用符合性预测来构建任务输出的预测间隔.
- 使用这些间隔的宽度量化测量和回收不确定性.
- 开发了局部适应性预测间隔,用于以后样本为基础的重建.
- 实施了多轮测量获取策略,以尽量减少不确定性.
主要成果:
- 证明了在用户指定的概率范围内保证任务输出限制的能力.
- 展示了针对特定下游任务量化的不确定性量化.
- 在加速磁共振成像 (MRI) 上验证了适应性测量策略.
结论:
- 提出的以任务为中心的方法有效地量化了图像重建中的不确定性.
- 合规预测为下游任务提供严格的不确定性保证.
- 基于任务不确定性的自适应性数据采集可以优化成像协议.
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