Technical Foundations

Abstract

With the rapid advancement of artificial intelligence and machine learning technologies, brain image analysis has entered a new development phase. This chapter systematically introduces the key technical foundations required for intelligent brain image analysis, primarily covering two core aspects: machine learning fundamentals and deep learning foundations[1, 2]. These technologies constitute the theoretical support and implementation basis for modern brain MRI analysis, providing a powerful technical framework for AI-based brain disease prediction. 1 Bradley J. Erickson et al. “Machine Learning for Medical Imaging”. In: RadioGraphics 37.2 (2017). PMID: 28212054, pp. 505–515. doi: https://www.w3.org/1999/xlink" ext-link-type="doi" xlink:href="https://dx.doi.org/10.1148/rg.2017160130">10.1148/rg.2017160130. eprint: https://www.w3.org/1999/xlink" ext-link-type="doi" xlink:href="https://dx.doi.org/10.1148/rg.2017160130">https://doi.org/10.1148/rg.2017160130. url: https://www.w3.org/1999/xlink" ext-link-type="doi" xlink:href="https://dx.doi.org/10.1148/rg.2017160130">https://doi.org/10.1148/rg.2017160130. 2 Alexander Selvikvåg Lundervold and Arvid Lundervold . “An overview of deep learning in medical imaging focusing on MRI”. In: Zeitschrift für Medizinische Physik 29.2 (2019). Special Issue: Deep Learning in Medical Physics, pp. 102–127. issn: 0939-3889. doi: https://www.w3.org/1999/xlink" ext-link-type="doi" xlink:href="https://dx.doi.org/10.1016/j.zemedi.2018.11.002">https://doi.org/10.1016/j.zemedi.2018.11.002. url: https://www.w3.org/1999/xlink" xlink:href="https://www.sciencedirect.com/science/article/pii/S0939388918301181">https://www.sciencedirect.com/science/article/pii/S0939388918301181.

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