Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis : Principles and Recent Advances

This work was supported in part by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT) under Grant NRF 2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program under Grant 20212023.Peer reviewedPublisher PD

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

Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional neural networks

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