# AutoML Systems For Medical Imaging

arXiv: https://arxiv.org/abs/2306.04750 · HTML: https://mlsanity.com/paper/2306.04750/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning](https://mlsanity.com/paper/2203.14542/) (2022)
- [An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection](https://mlsanity.com/paper/1810.10850/) (2018)
- [Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation](https://mlsanity.com/paper/1510.02795/) (2015)
- [Benchmarking Automatic Machine Learning Frameworks](https://mlsanity.com/paper/1808.06492/) (2018)
- [Augmentor: An Image Augmentation Library for Machine Learning](https://mlsanity.com/paper/1708.04680/) (2017)
- [T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging](https://mlsanity.com/paper/2111.07535/) (2021)
- [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)

## Built on by (most cited first, 1 of 1)

- [AutoMiSeg: Automatic Medical Image Segmentation via Test-Time Adaptation of Foundation Models](https://mlsanity.com/paper/2505.17931/) (2025)

## Abstract (arXiv)

The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. The combination of human expertise and computerized systems can result in improved diagnostic accuracy. An automated machine learning approach simplifies the creation of custom image recognition models by utilizing neural architecture search and transfer learning techniques. Medical imaging techniques are used to non-invasively create images of internal organs and body parts for diagnostic and procedural purposes. This article aims to highlight the potential applications, strategies, and techniques of AutoML in medical imaging through theoretical and empirical evidence.