■深度学习技术已广泛应用于医学图像分析。但由于自身成像原理的局限性,超声图像具有分辨率低、斑点噪声密度高等缺点,这不仅妨碍了患者病情的诊断,而且影响了计算机技术对超声图像特征的提取。
■在这项研究中,我们研究了深度卷积神经网络(CNN)分类的鲁棒性,分割,通过随机椒盐噪声和高斯噪声对乳腺超声图像进行目标检测。
■我们在8617张乳腺超声图像中训练并验证了9种CNN架构,但是用嘈杂的测试集测试了模型。然后,我们在这些乳腺超声图像中训练并验证了9种具有不同噪声水平的CNN架构,并用嘈杂的测试集测试了模型。我们数据集中的每个乳腺超声图像的疾病都由三名超声医师根据其恶性可疑性进行了注释和投票。用评价指标分别对神经网络算法的鲁棒性进行评价。
■当SaltandPepperNoise,斑点噪声,或高斯噪声被分别引入到图像中。因此,DenseNet,根据所选索引,选择UNet++和Yolov5作为最稳健的模型。当这三种噪声中的任何两种同时引入图像时,模型的准确性将受到很大影响。
■我们的实验结果揭示了新的见解:精度随噪声水平的变化趋势在每个用于分类任务和对象检测任务的网络中具有一些独特的特征。这一发现为我们提供了一种方法来揭示计算机辅助诊断(CAD)系统的黑盒体系结构。另一方面,本研究的目的是探讨在图像中直接添加噪声对神经网络性能的影响,这与现有的医学图像处理领域中关于鲁棒性的文章不同。因此,为今后评价CAD系统的鲁棒性提供了一种新的方法。
UNASSIGNED: Deep learning technology has been widely applied to medical image analysis. But due to the limitations of its own imaging principle, ultrasound image has the disadvantages of low resolution and high Speckle Noise density, which not only hinder the diagnosis of patients\' conditions but also affect the extraction of ultrasound image features by computer technology.
UNASSIGNED: In this study, we investigate the robustness of deep convolutional neural network (CNN) for classification, segmentation, and target detection of breast ultrasound image through random Salt & Pepper Noise and Gaussian Noise.
UNASSIGNED: We trained and validated 9 CNN architectures in 8617 breast ultrasound images, but tested the models with noisy test set. Then, we trained and validated 9 CNN architectures with different levels of noise in these breast ultrasound images, and tested the models with noisy test set. Diseases of each breast ultrasound image in our dataset were annotated and voted by three sonographers based on their malignancy suspiciousness. we use evaluation indexes to evaluate the robustness of the neural network algorithm respectively.
UNASSIGNED: There is a moderate to high impact (The accuracy of the model decreased by about 5%-40%) on model accuracy when Salt and Pepper Noise, Speckle Noise, or Gaussian Noise is introduced to the images respectively. Consequently, DenseNet, UNet++ and Yolov5 were selected as the most robust model based on the selected index. When any two of these three kinds of noise are introduced into the image at the same time, the accuracy of the model will be greatly affected.
UNASSIGNED: Our experimental results reveal new insights: The variation trend of accuracy with the noise level in Each network used for classification tasks and object detection tasks has some unique characteristics. This finding provides us with a method to reveal the black-box architecture of computer-aided diagnosis (CAD) systems. On the other hand, the purpose of this study is to explore the impact of adding noise directly to the image on the performance of neural networks, which is different from the existing articles on robustness in the field of medical image processing. Consequently, it provides a new way to evaluate the robustness of CAD systems in the future.