Alireza Rahi
Independent Research Scientist | Expert in Trustworthy AI & Medical Imaging | Brain Tumor Detection | Neurodegenerative Disorders | Genomics | Author of Pioneering Technical Books on ViT & U-Net
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Accurate cardiac MRI segmentation is essential for quantitative analysis of cardiac structure and function in clinical practice....
Brain metastases represent one of the most common intracranial malignancies, yet early and accurate detection remains challengin...
In this study, we applied an artificial intelligence–driven diagnostic framework to analyze gene expression profiles of patien...
In this study, we propose a stacked deep learning framework that integrates Convolutional Neural Networks (CNNs), Dense Neural N...
In this study, we propose a hybrid deep learning ensemble model for the automatic classification of dermoscopic images into beni...
n this study, we propose a fusion-based deep learning ensemble framework that integrates two well-established public ECG databas...
In this study, we propose an ensemble deep learning approach for classifying histopathological images of breast cancer using the...
The present book, “3D ViT in Medical Imaging: From Theory to TensorFlow Practice,” builds upon the foundations established i...
This book was written to guide readers through one of the most fascinating frontiers of modern science: the navigation of nanoro...
We present a global reference processed, merged, and balanced to ensure balanced gene expression dataset for fair representation...
Acute Myeloid Leukemia (AML) is a highly heterogeneous hematologic malignancy with multiple clinically relevant subtypes. Despit...
This book is dedicated to providing a comprehensive understanding of Vision Transformers (ViTs) and their applications in medica...
This book provides a comprehensive guide to U-Net and its applications in medical imaging. It covers deep learning architectures...
In this work, we introduce CAMUS-HeartNet, a deep meta-ensemble architecture combining multiple U-Net variants with a meta-learn...
This book is dedicated to providing a comprehensive understanding of Grad-CAM (Gradient-weighted Class Activation Mapping) and i...
we propose a robust machine learning framework for binary classification of cognitive task responses versus control conditions u...
we propose a deep learning–based framework for automated segmentation of cardiac chambers using the publicly available ACDC da...
