Alaa Sulaiman, CEO of Abdora

Co-Founder & CEO

Alaa Sulaiman, PhD

Alaa founded Abdora to build medical imaging AI that shows its work. If you can't explain why a model reached a conclusion, it shouldn't be used in medicine.

Background

Alaa holds a PhD in Artificial Intelligence from the National University of Malaysia, focused on medical image analysis and computational pathology. His doctoral work developed AI-driven methods for clinical imaging and pathology-based diagnosis.

His published research spans medical image segmentation, transformer architectures for clinical imaging, and AI-based cancer diagnosis — with work in Medical Image Analysis, MICCAI, and Heliyon (Cell Press).

At Abdora, he leads the technical vision and stays hands-on across the core reasoning engine, product decisions, and clinical partnerships.

Education

PhD in Artificial Intelligence

National University of Malaysia (UKM)

2014 – 2020  ·  Medical image analysis & computational pathology

Vision & Strategy

Setting direction for Abdora and making sure the team builds something that genuinely helps clinicians.

AI & Machine Learning

Hands-on with the models — especially explainable AI and reasoning for medical imaging.

Medical Imaging

Working across X-ray, CT, and MRI to build real diagnostic tools that earn clinical trust.

Healthcare Innovation

Turning rigorous AI research into products clinicians can use today.

“Radiologists deserve more than findings. They deserve the why. Abdora shows the evidence behind every detection, in plain language clinicians can trust and act on. We don't just detect. We explain.”

Alaa Sulaiman, PhD  ·  CEO & Co-Founder, Abdora

Selected Projects

Medical visual question answering calibration

Medical VQA Project

Medical visual question answering calibration

Reliable answer ranking and calibration for medical visual question answering on radiology and pathology images. Tackles confusable answers in open-ended clinical queries.

Semi-supervised pancreas segmentation with boundary correction

Pancreas Segmentation Project

Semi-supervised pancreas segmentation with boundary correction

Boundary-aware semi-supervised segmentation in CT scans. Corrects uncertain regions through distributional mutual learning and confidence gating.

Surgical video phase, tool and action understanding

Surgical Video Project

Surgical video phase, tool and action understanding

Spatio-temporal modeling for phase recognition, tool detection, and action understanding in endoscopic surgery videos. Enhances frozen foundation models with affinity-based refinement.

Pancreatic tumor segmentation in CT volumes

Pancreatic Tumor Segmentation Project

Pancreatic tumor segmentation in CT volumes

Lightweight multi-scale network for pancreatic tumor segmentation in CT. Handles extreme scale variability and irregular tumor shapes efficiently.

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