Meta-learning with Neural Architecture Search for Optimizing Medical Imaging Pipelines

DOI

10.2991/978-94-6463-700-7_24How to use a DOI?

Keywords

Medical Imaging; Meta-learning; Neural Architectures; Automated Machine Learning; Image Processing; Diagnosis

Abstract

The importance of optimizing image processing pipelines for precise diagnosis and therapy progress has been brought to the forefront by the explosive growth of medical imaging technology. This research shows how current methods fall short, particularly due to a lack of meta-learning strategies specifically designed to address medical imaging problems. In this paper, we introduce a new method for improving medical imaging algorithms by fusing meta-learning with the search for optimal neural architectures. The goal of our study is to create algorithms that make the exploration and selection of neural networks easier, so we're looking into the use of automated machine learning (AutoML) techniques to do so. While deep learning and transfer learning have been the focus of previous medical image analysis studies, it is still crucial to choose the most efficient image processing methods. Our technology presents an exciting new direction for the development of medical imaging, which is urgently needed in light of the ever-increasing data volumes and the demand for faster, more precise diagnosis.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Titu Singh Arora
AU  - Mohammed Abbas Qureshi
AU  - Gandam Vijay Kumar
PY  - 2025
DA  - 2025/04/19
TI  - Meta-learning with Neural Architecture Search for Optimizing Medical Imaging Pipelines
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 298
EP  - 307
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_24
DO  - 10.2991/978-94-6463-700-7_24
ID  - Arora2025
ER  -