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.

翻译：多年来，不变散射变换（IST）技术已成为医学图像分析领域的热门方法，包括利用卷积神经网络（CNN）进行小波变换计算，以捕获输入信号中模式的尺度和方向。IST旨在对医学图像中常见的变换（如平移、旋转、缩放和形变）保持不变性，从而提升分割、分类和配准等医学成像应用的性能，并可集成到机器学习算法中用于疾病检测、诊断和治疗规划。此外，将IST与深度学习方法相结合，有望发挥各自优势，增强医学图像分析效果。本研究通过梳理IST的类型、应用、局限性及未来研究方向，为研究人员和从业者提供了IST在医学成像中的全面概述。