AutoML Systems For Medical Imaging
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 7 Jun 2023.
Builds on
11 of 30 references.
References · 11
- 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
- 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
- 2017Deep Learning for Medical Image Processing: Overview, Challenges and Futurecited by 104
- 2022UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learningcited by 77
- 2018An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detectioncited by 30
- 2015Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentationcited by 24
- 2018Benchmarking Automatic Machine Learning Frameworkscited by 15
- 2017Augmentor: An Image Augmentation Library for Machine Learningcited by 14
- 2021T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imagingcited by 11
- 2022Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysiscited by 9
- 2021The Prominence of Artificial Intelligence in COVID-19cited by 7
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Citing papers per year: 2025: 1.
1 paper.