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Vera Damerjian Pieters, PhD

Advising MedTech teams toward defensible & robust AI decisions.

I advise medical imaging AI teams on how to frame, evaluate, and de-risk their R&D initiatives, at the stages where early decisions shape everything downstream.

My work spans data readiness assessment, experiment and evaluation review, state-of-the-art positioning, and independent technical analysis.

With a PhD in Computer Vision and over 10 years of applied MedTech experience, I bring both the technical depth and the independent perspective needed to support defensible decision-making.

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How I can support you

Advisory

Feasibility clarification, risk identification, and support for defensible technical choices throughout R&D, through targeted interventions or ongoing advisory engagements.

Focused on technical and problem framing, data readiness, experiment design, and evaluation practices for medical imaging AI teams.

Resources

Practical frameworks, reference materials, and video courses designed for medical imaging AI teams, built to surface risks early and support more defensible technical decisions.

Skill Transfer

Advanced training programs in medical imaging AI, computer vision, and responsible AI, grounded in applied R&D practice and tailored to technical teams, academic settings, or professionals looking to build rigorous, actionable expertise.

PhD in Computer Vision, with over 10 years of applied MedTech R&D experience
Deep familiarity with the research-to-clinical gap, where scientific rigor meets real-world constraints
Hands-on experience leading AI projects and collaborating across multidisciplinary teams
Independent advisory posture, providing structured and unbiased technical input
Fluent with both technical and executive stakeholders, supporting informed decision-making at all levels
Bilingual (English/French), with experience across academic, clinical, and industrial contexts

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

Insights grounded in applied research, real-world projects, and hands-on technical work with medical imaging teams.

Problem Framing in Medical Imaging AI

Two teams can work on the same medical imaging task and end up building very different systems. The difference often starts before any model is trained, at the moment the problem is defined. This article explores what problem framing actually means, where ambiguity comes from, and what a well-framed problem...

Scalable Dataset Triage in Medical Imaging AI

Scalable Dataset Triage in Medical Imaging AI Teams often receive large imaging datasets from multiple hospitals or partners. At first glance, the dataset may look ready for model development: thousands of images, structured folders and metadata. However, the reality is usually more complex. Some images may not correspond to the...

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: 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.

What’s the Real Difference between Image Processing, Analysis, and Computer Vision?

Terms like image processing, image analysis, and computer vision are often used interchangeably, but do they really mean the same thing?