AutoML Systems For Medical Imaging
This work addresses the problem of improving healthcare quality for physicians and patients through automated machine learning in medical image analysis, but it appears incremental as it builds on existing AutoML techniques.
The paper explores how AutoML, using neural architecture search and transfer learning, can simplify the creation of custom image recognition models for medical imaging, aiming to enhance diagnostic accuracy by combining human expertise with computerized systems.
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.