# Framelet Pooling Aided Deep Learning Network : The Method to Process High Dimensional Medical Data

## TL;DR

This paper introduces a framelet-pooling method that transforms high-dimensional medical data into low-dimensional components, significantly reducing computational costs while maintaining learning performance.

## Contribution

The paper presents a novel framelet-pooling approach that mitigates computational complexity in deep learning for high-dimensional medical data, enabling efficient processing.

## Key findings

- Reduces computational costs by decomposing large data into smaller tasks
- Maintains comparable accuracy to standard methods
- Significantly decreases neural network complexity

## Abstract

Machine learning-based analysis of medical images often faces several hurdles, such as the lack of training data, the curse of dimensionality problem, and the generalization issues. One of the main difficulties is that there exists computational cost problem in dealing with input data of large size matrices which represent medical images. The purpose of this paper is to introduce a framelet-pooling aided deep learning method for mitigating computational bundle, caused by large dimensionality. By transforming high dimensional data into low dimensional components by filter banks with preserving detailed information, the proposed method aims to reduce the complexity of the neural network and computational costs significantly during the learning process. Various experiments show that our method is comparable to the standard unreduced learning method, while reducing computational burdens by decomposing large-sized learning tasks into several small-scale learning tasks.