Women's & Fetal Health Imaging |

Reducing Clinician Annotation Fatigue in Open-Set Federated Learning: A Client-Adaptive Vision-Language Gatekeeper

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

K-MaT: Knowledge-Anchored Manifold Transport for Cross-Modal Prompt Learning in Medical Imaging

Sun, 27 Sep 2026 00:00:00 +0000

When multi-institutional dataset aggregation masks shortcut-like behavior in Foundation Models for medical imaging

Tue, 01 Sep 2026 00:00:00 +0000

Bias and generalizability of foundation models across datasets in breast mammography

Wed, 01 Jan 2025 00:00:00 +0000

Breast Mammography Dataset (Lebanon & international cohorts)

Wed, 01 Jan 2025 00:00:00 +0000

Partner: Dr. Charbel Mourad. Basis of our MICCAI 2025 study on bias and generalisability of foundation models across mammography datasets.

Fetal Ultrasound Dataset for Congenital Diaphragmatic Hernia

Wed, 01 Jan 2025 00:00:00 +0000

Partner: PD Dr. Florian Recker, Center for Obstetrics and Gynecology, University Hospital Bonn.

Bias-aware foundation models for equitable breast cancer screening

Mon, 01 Jan 2024 00:00:00 +0000

This project addresses fairness and generalization challenges of foundation models for breast mammography, where AI systems trained predominantly on data from high-income countries often underperform in underrepresented populations. Together with international clinical partners, we assembled and analyzed a heterogeneous collection of mammography datasets from Europe, North America, the Middle East, and Asia, including a biopsy-confirmed in-house cohort of approximately 1,500 patients from Lebanon. Methodologically, we conducted a systematic evaluation of foundation models under cross-dataset shift and demonstrated that naïve dataset aggregation improves average performance but does not eliminate subgroup-specific biases. Fairness-aware optimization strategies substantially reduced performance disparities across age groups, breast density categories, and geographic regions while maintaining competitive diagnostic accuracy.

Perspective

The collaboration constitutes an ongoing international research effort at the intersection of medical imaging, fairness-aware AI, and global health. Building on current results, the project is being expanded to additional clinical partners in Palestine and Egypt, supported by the Global One Health Research Academy (GLOHRA), and is planned to scale further through the submitted EU Horizon Europe project SHE-Scan, contingent on funding.

Two-Steps Approach for Breast Cancer Detection and Classification Using Convolutional Neural Networks.

Mon, 01 Jan 2024 00:00:00 +0000

Evolving the era of 5D ultrasound? A systematic literature review on the applications for artificial intelligence ultrasound imaging in obstetrics and gynecology

Sun, 01 Jan 2023 00:00:00 +0000

Deep learning for fetal diaphragmatic hernia detection in ultrasound

Wed, 01 Jun 2022 00:00:00 +0000

Congenital diaphragmatic hernia (CDH) is a rare but severe fetal malformation associated with high neonatal morbidity and mortality, where early detection is critical for risk stratification, parental counseling, and perinatal management. Although ultrasound is the primary imaging modality in prenatal screening, CDH detection remains challenging due to limited acoustic windows, overlapping anatomical structures, and strong operator dependence.

The initial objective of this project was to develop deep learning–based methods for automated CDH detection in fetal ultrasound. Due to changes in the clinical collaboration context, the project could not be continued in its originally planned data-driven form. As a result, the focus shifted toward consolidating the existing evidence base and systematically analyzing methodological and clinical barriers for AI-assisted fetal ultrasound diagnostics.

As a central outcome, we co-authored a comprehensive systematic review on artificial intelligence in obstetric and gynecologic ultrasound, critically assessing methodological rigor, data availability, validation practices, and clinical readiness across 189 published studies. The review identified fetal malformation detection, including CDH, as a clinically high-impact yet underexplored application area, highlighting the need for standardized datasets, multi-center validation, and interpretable AI approaches.

Perspective

The insights gained from this project provided a methodological foundation for subsequent ultrasound- and MRI-based research with sustained clinical partnerships and informed the design of later projects with improved data availability and validation strategies. Building on this groundwork, renewed efforts toward CDH detection are envisaged within a national multi-center collaboration, leveraging harmonized data collection and shared clinical expertise.