# Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

## TL;DR

This survey reviews recent deep learning advances in medical image analysis, explaining core concepts for non-experts, categorizing literature by tasks and anatomy, and highlighting challenges like data annotation and future research directions.

## Contribution

It provides a comprehensive, accessible overview of deep learning in medical imaging, integrating computer vision perspectives and identifying key challenges and future opportunities.

## Key findings

- Deep learning has significantly impacted medical image analysis.
- Lack of large annotated datasets is a major challenge.
- Future directions include leveraging mature techniques from related fields.

## Abstract

Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in `Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out `lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.