# Invariant Scattering Transform for Medical Imaging

arXiv: https://arxiv.org/abs/2307.04771 · HTML: https://mlsanity.com/paper/2307.04771/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 68 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [Parametric Scattering Networks](https://mlsanity.com/paper/2107.09539/) (2021)
- [Active Learning on Medical Image](https://mlsanity.com/paper/2306.01827/) (2023)
- [Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis](https://mlsanity.com/paper/2210.13336/) (2022)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [Generative Adversarial Networks for Data Augmentation](https://mlsanity.com/paper/2306.02019/) (2023)
- [Introduction to Medical Imaging Informatics](https://mlsanity.com/paper/2306.00421/) (2023)
- [Case Studies on X-Ray Imaging, MRI and Nuclear Imaging](https://mlsanity.com/paper/2306.02055/) (2023)
- [Iris Recognition Using Scattering Transform and Textural Features](https://mlsanity.com/paper/1507.02177/) (2015)
- [CNN-Based Invertible Wavelet Scattering for the Investigation of Diffusion Properties of the In Vivo Human Heart in Diffusion Tensor Imaging](https://mlsanity.com/paper/1912.07776/) (2019)
- [Operational Learning-based Boundary Estimation in Electromagnetic Medical Imaging](https://mlsanity.com/paper/2108.03233/) (2021)
- [Incongruity Detection between Bangla News Headline and Body Content through Graph Neural Network](https://mlsanity.com/paper/2211.07709/) (2022)
- [Noise-resilient approach for deep tomographic imaging](https://mlsanity.com/paper/2211.15456/) (2022)

## Abstract (arXiv)

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.