Medical-Imaging on Rachid Youven Zeghlache

Medical Image Analysis

Thu, 01 Jan 2026 00:00:00 +0000

Overview

Medical image analysis is the computational backbone of my research. Before building longitudinal or predictive models, we need robust methods for understanding what is in a single image: detecting lesions, quantifying biomarkers, segmenting anatomical structures. In my work the primary modalities are ophthalmological.

Imaging modalities

Colour Fundus Photography

Wide-field retinal photographs capture the optic disc, macula, vessels, and peripheral retina. DR grading, vessel segmentation, and optic disc detection are well-established tasks. I use fundus images as the primary modality in several longitudinal and progression prediction pipelines.

Multi-modal Learning

Thu, 01 Jan 2026 00:00:00 +0000

Overview

Multi-modal learning addresses one of the fundamental challenges in medical AI: clinical decisions are rarely made from a single data source. A clinician diagnosing diabetic macular oedema consults fundus photographs, OCT B-scans, fluorescein angiography, and the patient’s longitudinal record simultaneously. My research develops deep learning architectures that can fuse these heterogeneous modalities into a coherent representation.

Key research directions

Standard concatenation of modality-specific features often fails because different modalities live in incompatible representation spaces. I explore contrastive objectives and cross-attention mechanisms that align representations across modalities without requiring paired data at every follow-up visit.

MARIO AMD Progression Challenge

Sun, 01 Sep 2024 00:00:00 +0000

Overview

The MARIO AMD Progression Challenge was held at MICCAI 2024 and focused on the automated assessment of Age-related Macular Degeneration (AMD) progression using deep learning.

We are pleased to announce the publication of our comprehensive analysis of the challenge results, and the corresponding dataset is now publicly available for the research community.

Research Focus

This challenge addressed two key clinical questions:

Progression prediction: Will a patient’s AMD progress over the next 12 months?
Visual acuity change: Will the patient’s visual acuity improve, stabilise, or worsen?

Dataset

The MARIO dataset provides:

Medical-Imaging on Rachid Youven Zeghlache

Medical Image Analysis

Overview

Imaging modalities

Colour Fundus Photography

Multi-modal Learning

Overview

Key research directions

Cross-modal feature alignment

MARIO AMD Progression Challenge

Overview

Research Focus

Dataset