Hassan Alhajj

• Lead development and validation of MRI-based breast cancer image-processing

pipelines, aligning cross-functional teams (AI, imaging science, software) to enhance

lesion detection accuracy.

• Designed and delivered an AI-driven ultrasound screening application, integrating

deep learning models for nodule segmentation and classification, improving early

stage cancer detection rates.

• Drove multi-modal fusion of MRI, mammography, and ultrasound data, boosting

diagnostic accuracy within the breast cancer analysis system by integrating

complementary imaging features.

1) Currently working as part of an international collaboration between Zeiss, Evolucare, Adcis and LaTIM on a RHU project where the main objective is to push further the limits of diabetic retinopathy diagnosis using deep learning for multi modalities.

2) Worked on Photoplethysmogram (PPG) and electrocardiogram (ECG) analysis using deep learning for better prognosis prediction.

3) Designed an eye surgery simulator and trained an agent to perform surgical tasks using reinforcement learning algorithms.

4) Co-organized three image analysis challenges CATARACTS 2017, EndoVis/CATARACTS 2018 and EndoVis/CATARACTS 2020, tackling the task of object presence detection and surgical activity recognition in roughly 2 million of surgery images.

1) Worked as part of an international teams developing news and full franchise

management applications iOS for companies in US.

2) Prepared project plans, tasks estimations, requirement analysis and design

documents.

3) Participated in international team meetings to discuss milestones and deliveries.