# A Survey on Deep Learning in Medical Image Analysis

## TL;DR

This survey reviews recent advances in deep learning applications for medical image analysis, covering various tasks and highlighting current challenges and future research directions.

## Contribution

It provides a comprehensive overview of over 300 recent studies on deep learning in medical imaging, summarizing key methodologies and applications.

## Key findings

- Deep learning is increasingly used for classification, detection, and segmentation in medical images.
- Major challenges include data scarcity and model interpretability.
- Future directions involve improving model robustness and clinical integration.

## Abstract

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.