噪音(视频)
计算机科学
校准
人工智能
混淆矩阵
人工神经网络
数据挖掘
模式识别(心理学)
噪声测量
机器学习
医学影像学
计算机视觉
图像(数学)
统计
降噪
数学
作者
Coby Penso,Lior Frenkel,Jacob Goldberger
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2024-01-15
卷期号:43 (6): 2050-2060
被引量:2
标识
DOI:10.1109/tmi.2024.3353762
摘要
A classification model is calibrated if its predicted probabilities of outcomes reflect their accuracy. Calibrating neural networks is critical in medical analysis applications where clinical decisions rely upon the predicted probabilities. Most calibration procedures, such as temperature scaling, operate as a post processing step by using holdout validation data. In practice, it is difficult to collect medical image data with correct labels due to the complexity of the medical data and the considerable variability across experts. This study presents a network calibration procedure that is robust to label noise. We draw on the fact that the confusion matrix of the noisy labels can be expressed as the matrix product between the confusion matrix of the clean labels and the label noises. The method is based on estimating the noise level as part of a noise-robust training method. The noise level is then used to estimate the network accuracy required by the calibration procedure. We show that despite the unreliable labels, we can still achieve calibration results that are on a par with the results of a calibration procedure using data with reliable labels.
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