Invariant scattering transform introduces new area of research that merges
the signal processing with deep learning for computer vision. Nowadays, Deep
Learning algorithms are able to solve a variety of problems in medical sector.
Medical images are used to detect diseases brain cancer or tumor, Alzheimer's
disease, breast cancer, Parkinson's disease and many others. During pandemic
back in 2020, machine learning and deep learning has played a critical role to
detect COVID-19 which included mutation analysis, prediction, diagnosis and
decision making. Medical images like X-ray, MRI known as magnetic resonance
imaging, CT scans are used for detecting diseases. There is another method in
deep learning for medical imaging which is scattering transform. It builds
useful signal representation for image classification. It is a wavelet
technique; which is impactful for medical image classification problems. This
research article discusses scattering transform as the efficient system for
medical image analysis where it's figured by scattering the signal information
implemented in a deep convolutional network. A step by step case study is
manifested at this research work.

研究发现，不变散射变换是一种有效的医学图像分析系统，可通过将信号信息分散到深度卷积网络中，通过波波变换技术来构建有用的图像分类信号表征。

医学图像不变散射变换

Invariant Scattering Transform for Medical Imaging

Over the years, the Invariant Scattering Transform (IST) technique has become
popular for medical image analysis, including using wavelet transform
computation using Convolutional Neural Networks (CNN) to capture patterns'
scale and orientation in the input signal. IST aims to be invariant to
transformations that are common in medical images, such as translation,
rotation, scaling, and deformation, used to improve the performance in medical
imaging applications such as segmentation, classification, and registration,
which can be integrated into machine learning algorithms for disease detection,
diagnosis, and treatment planning. Additionally, combining IST with deep
learning approaches has the potential to leverage their strengths and enhance
medical image analysis outcomes. This study provides an overview of IST in
medical imaging by considering the types of IST, their application,
limitations, and potential scopes for future researchers and practitioners.

本研究综述了应用不变散射变换（IST）在医学图像分析中的类型、应用、限制以及未来研究和实践的潜在范围，并考虑采用卷积神经网络进行小波变换计算以捕获输入信号的模式，以提高医学成像应用程序的性能，例如分割，分类和配准，可集成到机器学习算法中用于疾病检测，诊断和治疗规划，并进一步展示了将 IST 与深度学习方法相结合的潜力，以增强医学图像分析结局。