Window settings to rescale and contrast stretch raw data from radiographic images such as Computed Tomography (CT), X-ray and Magnetic Resonance images is a crucial step as data pre-processing to examine abnormalities and diagnose diseases. We propose a distant-supervised method for determining automatically the best window settings by attaching a window estimator module (WEM) to a deep convolutional neural network (DCNN)-based lesion classifier and training them in conjunction. Aside from predicting a flexible window setting for each raw image, we statistically identify the top four window settings by calculating the mean and standard deviations for the entire dataset. Images are scaled on each of the top settings estimated by WEM and following lesion classifiers are subsequently trained. We study the effects of only using the flexible window, the single fixed window as either a known default window used by radiologists or an estimated mean value, and two different approaches to combine results from the top window settings to improve the detection of intracranial hemorrhage (ICH) from brain CT images. Experimental results showed that using the top four window settings identified from the window estimator module and combining the results had the best performance.

窗口设置用于重新缩放和对比度拉伸来自计算机断层扫描 (CT)、X 射线和磁共振图像等放射图像的原始数据，是作为数据预处理检查异常和诊断疾病的关键步骤。我们提出了一种远程监督方法，通过将窗口估计器模块 (WEM) 附加到基于深度卷积神经网络 (DCNN) 的病变分类器并结合训练它们来自动确定最佳窗口设置。除了为每个原始图像预测灵活的窗口设置之外，我们还通过计算整个数据集的均值和标准差来统计确定前四个窗口设置。图像在 WEM 估计的每个顶级设置上进行缩放，随后对以下病变分类器进行训练。我们研究了只使用灵活窗口的效果，单个固定窗口作为放射科医生使用的已知默认窗口或估计平均值，以及两种不同的方法来组合来自顶部窗口设置的结果，以改进从脑 CT 图像中检测颅内出血 (ICH)。实验结果表明，使用从窗口估计器模块中识别出的前四个窗口设置并组合结果具有最佳性能。