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）技术已广泛应用于医学图像分析，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）具有不变性，从而提升分割、分类和配准等医学影像应用性能，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，有望发挥各自优势并增强医学图像分析效果。本研究综述了IST在医学影像中的应用，涵盖IST类型、应用场景、局限性及未来研究与实践的潜在方向。