Home - VeraDP

Vera Damerjian Pieters, PhD

Scientific & Data Advisory for robust Medical Imaging R&D

I advise medical imaging AI teams on complex R&D questions, bringing scientific rigor to decisions around data, experimentation, and evaluation.

My work connects scientific and technical framing, actionable literature review, deep data understanding, and rigorous experimentation and evaluation.

With a PhD in Computer Vision and over 10 years of applied medical imaging R&D experience, I help teams make evidence-based decisions grounded in scientific research, deep data understanding, and real-world R&D constraints.

Get in touch

Deep focus on data understanding and qualification

Data shape what can be developed, evaluated, and ultimately supported by evidence. Understanding their provenance, composition, variability, annotations, and limitations is therefore a foundation for sound medical imaging R&D.

I qualify data against the R&D question at hand to determine how they can reliably support development, experimentation, and evaluation decisions.

How I can support you

Scientific R&D Advisory

Scientific and data advisory for medical imaging AI teams facing complex R&D questions, from problem framing and data qualification to experimentation and evaluation.

Available through targeted engagements or ongoing advisory, depending on the question and level of support needed.

Resources

Practical resources for medical imaging AI R&D, translating scientific and technical questions into structured insights, methods, and reference materials.

Skill Transfer

Advanced training in AI and Computer Vision, from core technical foundations to specialized Medical Imaging AI topics, grounded in scientific research and real-world R&D practice.

VeraDP is a registered training provider in France.

PhD in Computer Vision, with over 10 years of applied MedTech R&D experience
Deep familiarity with the research-to-clinical gap and ability to speak directly with clinicians to keep technical decisions grounded in clinical reality
A rigor for defensible decisions: justifying, benchmarking, and documenting technical choices so they hold up to scrutiny
Hands-on experience leading AI projects and collaborating across multidisciplinary teams
Structured advisory approach, bringing clarity and scientific rigor to complex R&D questions
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

Get in touch

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

Insights grounded in applied research, scientific literature, and real-world medical imaging R&D.

Featured articles

How Absolute is the “Ground Truth” in Medical AI Annotation?

In architecting safe-by-design systems, trustworthy AI must be addressed at every stage of the development lifecycle. One of the most persistent assumptions is the absolute certainty of medical annotation.

Can Metadata be trusted as Ground Truth for Medical Imaging AI?

Metadata is often used as ground truth in medical imaging AI, but its availability does not guarantee its reliability. This article explores how a metadata traceability assessment can reveal inconsistencies, missing information and manufacturer-dependent limitations before model development begins.

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.