Starting June 13, 2026

AI in
Medical Imaging
Summer Series

A weekly online deep-dive into AI and machine learning for medical imaging , from the ML cycle and SVMs all the way to UNet, HRNet, and state-of-the-art medical models.

Every Saturday 6:00 PM Beirut Time Online 30 Spots Max

AIxMED Summer Saturdays

First Cohort · Summer 2026

Machine Learning Cycle
Hands-on Scikit-Learn & SVM
NNs, CNNs & Transformers
Medical Image Modalities
Medical Tasks & Hands-on Models
BioMedCLIP, RAD DINO, UNet
Nvidia Generative Models
Capstone Project

$25

First cohort

30 spots only

Curriculum

What You Will Learn

A hands-on progression from ML foundations to the architectures powering modern medical AI systems.

Session 01

Machine Learning Cycle

End-to-end ML workflow: problem framing, data preparation, model training, evaluation, and iteration.

Session 02

Hands-on Scikit-Learn & SVM

Support Vector Machines and classical ML in practice using Scikit-Learn on real datasets.

Session 03

Intro to Neural Networks

Perceptrons, backpropagation, activation functions, and training dynamics from the ground up.

Session 04

CNNs & Transformers

Convolutional networks for image features and Vision Transformers for global context in medical imaging.

Session 05

Medical Image Modalities

X-ray, MRI, CT, ultrasound, and pathology slides — characteristics, challenges, and preprocessing.

Session 06

Medical Tasks

Classification, regression, segmentation, and generation as the four core paradigms in medical AI.

Session 07

Classification: BioMedCLIP

Hands-on classification using BioMedCLIP — a vision-language model trained on biomedical image-text pairs.

Session 08

Regression: RAD DINO

Hands-on regression for radiology with RAD DINO, covering dose and outcome prediction from imaging data.

Session 09

Segmentation: UNet

Hands-on medical image segmentation using UNet — the gold-standard encoder-decoder architecture.

Session 10

Generation: Nvidia Models

Generative AI for medical imaging using Nvidia's medical AI models for synthetic data and augmentation.

Capstone

Project

Apply the full pipeline to a real medical imaging problem of your choice, from data to a working model.

Format

How It Works

Structured to fit busy professionals while keeping sessions deep and hands-on.

100% Online

Live sessions streamed online. Join from anywhere in the world.

Every Saturday

Consistent weekly schedule starting June 13, 2026 at 6:00 PM Beirut time.

Hands-on Code

Every session includes live coding, exercises, and real medical imaging examples.

Small Cohort

Maximum 30 participants for a focused, interactive learning experience.

Instructor

Meet Your Instructor

Ahmad Mustapha

Doctoral Researcher · American University of Beirut

Ahmad is a doctoral researcher at AUB specializing in AI for medical imaging, with a focus on unsupervised deep learning, representation learning, and bias in medical AI systems. He has led and contributed to projects spanning X-ray disease detection, vehicle detection in the wild, and multi-service AI platforms. His mission is to make cutting-edge medical AI accessible to clinicians, researchers, and engineers alike.

Google Scholar LinkedIn GitHub Email

Pricing

Simple, Transparent Pricing

Early-cohort pricing , spots are strictly limited.