Invariant Scattering Transform for Medical Imaging
This is an incremental review paper summarizing existing IST methods for medical imaging applications like disease detection.
The paper provides an overview of the Invariant Scattering Transform (IST) technique for medical image analysis, highlighting its use in improving performance for tasks like segmentation and classification by achieving invariance to common transformations such as translation and rotation.
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.