<?xml version="1.0" encoding="utf-8"?><rss version="2.0"><channel><title>Rajakumar Girija, S.L. Jayalakshmi, R. Vedhapriyavadhana</title><link>https://shop.theiet.org:443/author/rajakumar-girija-s-l-jayalakshmi-r-vedhapriyavadhana</link><description>Rajakumar Girija, S.L. Jayalakshmi, R. Vedhapriyavadhana</description><item><title>Deep Learning Models in Medical Imaging</title><link>https://shop.theiet.org:443/deep-learning-models-in-medical-imaging</link><description>&lt;p xmlns="http://ns.editeur.org/onix/3.0/reference"&gt;As healthcare technology moves towards a near-human level of performance in image recognition for screening, diagnosis, staging, and prognosis, this authored book lays the groundwork for understanding AI's transformative impact on the analysis of medical images.&lt;/p&gt;
&lt;p xmlns="http://ns.editeur.org/onix/3.0/reference"&gt;The book introduces fundamental AI concepts - machine learning and deep learning - and outlines how they are applied in diagnostic automation. Readers are introduced to common imaging techniques like X-ray, MRI, CT, and ultrasound, as well as their integration into clinical settings. Historical milestones, current technologies, and deployment challenges are discussed alongside the associated legal, ethical, and bias-related issues.&lt;/p&gt;
&lt;p xmlns="http://ns.editeur.org/onix/3.0/reference"&gt;The book covers the complete pipeline from image acquisition to model deployment in clinical settings. Special focus is given to lightweight, explainable AI (XAI) systems suitable for resource-constrained healthcare environments.&lt;/p&gt;
&lt;p xmlns="http://ns.editeur.org/onix/3.0/reference"&gt;The authors explore diagnostic applications such as diabetic retinopathy screening using fundus images, skin lesion classification, interpretation of X-ray images, mammography, and brain scans. Novel techniques including federated learning, self-supervised models, and domain adaptation for diagnostic robustness are thoroughly explored, highlighting the global significance of AI-driven healthcare.&lt;/p&gt;
&lt;p xmlns="http://ns.editeur.org/onix/3.0/reference"&gt;&lt;i&gt;Deep Learning Models in Medical Imaging: Diagnostic innovations and clinical intelligence&lt;/i&gt; is suitable for an audience of AI researchers in healthcare, biomedical engineers, diagnostic staff and others working in medical imaging and data science.&lt;/p&gt;</description><pubDate>Tue, 08 Sep 2026 08:21:54 GMT</pubDate><guid isPermaLink="true">https://shop.theiet.org:443/deep-learning-models-in-medical-imaging</guid></item></channel></rss>