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Vera Damerjian Pieters, PhD

Advising MedTech teams on AI, data, and R&D decisions

I am a Computer Vision specialist with a PhD and over a decade of experience across academic research and applied AI in MedTech.

I have led and reviewed AI projects at the intersection of research, data, and real-world constraints, working closely with technical teams and decision-makers.

Today, I advise organizations on how to frame, evaluate, and de-risk AI initiatives, particularly during R&D phases where early technical and data decisions shape everything downstream.

Alongside consulting, I also teach and mentor in academic and professional settings, with a focus on making complex technical concepts rigorous, structured, and actionable.

How I can support you

Courses & Workshops

I design and teach high-quality, hands-on programs in AI and computer vision. I adapt the content to varying levels of expertise and academic or professional contexts.

Whether you are a school, a company, or an individual, I help demystify complex topics and make them relevant, engaging, and directly applicable.

Consulting

I advise MedTech teams on technical framing, experiment design, and data-driven decision-making in computer vision projects.

My work focuses on clarifying feasibility, evaluating assumptions, and supporting sound technical choices during R&D – through targeted interventions or ongoing advisory roles.

PhD-level expertise in computer vision, with over 10 years of applied MedTech R&D experience
Proven track record leading AI projects and collaborating across multidisciplinary teams
Strong experience at the research-industry interface, where scientific rigor meets real-world constraints
Capacity to frame and evaluate complex technical problems, including data readiness, experiment design, and technical feasibility
Independent advisory posture, providing structured and unbiased technical input
Clear communication with technical and executive stakeholders, supporting informed decision-making across all levels
Bilingual (English/French), with experience working with international teams across academic, clinical, and industrial contexts

Get in touch

Writing on AI, data, and decision-making in medical imaging

Insights grounded in applied research, data audits, and real-world AI projects.

Data Distillation in Medical Imaging

Data Distillation in Medical Imaging Reducing Data Dependency · Part 3 Medical imaging faces persistent data challenges: strict privacy constraints, high annotation costs, limited and imbalanced datasets, and the logistical burden of storage and inter-site data transfer. These issues were discussed in detail in the Data Bottleneck article. As the demand for...

Medical Image Augmentation & Synthetic Data

Augmentation and synthetic data now play a central role in medical imaging AI, expanding datasets, improving robustness and enabling new applications such as domain translation, privacy preservation and rare-case simulation.

Training Models With Little or No Labels

This article explores label-efficient learning approaches: semi-, unsupervised, and self-supervised learning, that enable AI models to generalize better in data-scarce domains such as medical imaging.

The Data Bottleneck in Medical Imaging

The Data Bottleneck in Medical Imaging Medical imaging AI has achieved remarkable progress, but most breakthroughs still rely on massive, labeled datasets: a luxury often out of reach in healthcare. Working with large-scale data is a major bottleneck in medical imaging projects. There are too many factors at play when...

Data Auditing: The Foundation of Responsible AI in Medical Imaging

Before training any neural network, before tuning hyperparameters or optimizing inference times, the first responsible step is to audit your data.

Understanding AI Values: The Meanings, Subtleties & Nuances

AI ethics comes with a growing set of buzzwords: responsible, ethical, fair, transparent, explainable, and more. Some overlap, others differ in subtle ways. This post breaks down the key terms, explains their nuances, and points you to serious resources for deeper exploration.

Dice Coefficient vs. IoU in Medical Image Segmentation

Dice Coefficient and Intersection over Union (IoU) are widely used to evaluate medical image segmentation. Both measure overlap between prediction and ground truth but behave differently. This post explains their differences, strengths, and offers guidance on choosing the right metric.