MRI Sequence Selection for Brain Tumor Segmentation
Brain-tumour segmentation is multi-parametric. Which MRI sequences a model needs, and how to harmonise them across scanners.
GetDATA Team ·
Blog
Guides and insights on medical data, AI and research.
Building Balanced CT Datasets for Oncology Research
Tumour findings are rare and imbalanced. How to design a CT cohort that trains a model to catch the cases that matter.
GetDATA Team ·
DICOM vs NIfTI: Choosing the Right Format for Your ML Pipeline
DICOM is the clinical source of truth; NIfTI is the research workhorse. When to use each, and the conversion pitfalls to avoid.
GetDATA Team ·
De-identification of Medical Imaging: A Step-by-Step Checklist
Removing PHI means cleaning both the DICOM header and the pixels. A practical checklist plus the HIPAA/GDPR baseline.
GetDATA Team ·
Chest X-ray Datasets for Medical AI: A Practical Buyer's Guide
Chest radiography is cheap and ubiquitous — and full of shortcut signals. Here is what to check before you train on a CXR dataset.
GetDATA Team ·
How to Source ECG Datasets for Arrhythmia Detection Models
What separates a training-ready 12-lead ECG cohort from a noisy one — labels, lead configuration, class balance and compliant sourcing.
GetDATA Team ·
De-identification of Medical Imaging, Explained
How patient data is removed from imaging studies, and what researchers should verify before training on them.
GetDATA Team ·
Best ECG Datasets for AI Research in 2026
A practical guide to sourcing high-quality, well-labelled 12-lead ECG datasets for machine-learning projects.
GetDATA Team ·