Foundation models
& vision
Adapt foundation models for classification, detection and segmentation. Benchmark against the baselines that matter.
Computer vision & data science
AI & ML
engineering.
Expertise that delivers.
Computer vision, medical time series and ML pipelines, built for your data and software.
Working with teams worldwide
Data to clinical decision support
Input images
PreprocessingNormalize & tile
MODEL / CLASSIFICATION
Quality flag
Review artifact
classificationSegmentation overlay
REVIEWING RELIABILITY
Forecast · Illustrative uncertaintyHow certain is the output?
Calibration, robustness and domain checks
uncertainty reviewNEVYON DATALABS
Image review summary
InputSynthetic image
OutputIllustrative region mask
ReviewHuman review required
Image & mask linked
exampleworkflow
Computer visionAI & MLData scienceDataOpsMLOps
Applied expertise
Built around
your use case.
Specialist AI engineering, built around your data and delivery goals.
Foundation models
Low-latency
ML pipelines
Optimize data loading and inference. Build services around your latency and throughput targets.
Privacy-preserving
AI
Keep sensitive data within agreed boundaries through de-identification, federated learning and hybrid-cloud design.
Real-world data
& prediction
Build predictive models from clinical and observational data. Check quality, leakage and generalization.
Computational pathology
A complete view
of every slide.
Whole-slide processing, artifact detection and tissue analysis in one pipeline.
01
Whole-slide images
Read large images and identify tissue regions.
02
Preprocess & patch
Extract patches, check artifacts and handle stain variation.
03
Inference and segmentation
Develop classification or segmentation and quantify uncertainty.
04
QC & review
SLIDE QC
Blur 18.6%
Blood 7.4%
Damage 12.2%
exampleLink regions, quality flags and uncertainty to clinical review.
Whole slide → Preprocessing → Inference → QC report
Synthetic overview, patch sampling and artifact-mapping examplesExplore the artifact map
Pathology / Interactive exploration
See what’s hiding
in the input.
Move over the slide to reveal artifacts, or select a region below.
example
Radiology & medical imaging
Breast MRI
preprocessing.
Prepare contrast phases for lesion analysis and clinical review.
01
Phase matching
Contrast phases
Keep contrast phases in order.
02
Bias field correction
BeforeAfter
N4 correction for intensity shading.
03
Co-registration
Co-registration overlay
A shared spatial reference.
04
Intensity normalization
BeforeAfter
Bring intensities to a common scale.
05
Isotropic resampling
Input gridUniform grid
Use a common voxel spacing.
01 / 05
Quality review
Motion · Low SNR · Coil shading
Breast DCE-MRI / Interactive illustration
Follow the contrast.
Keep the context.
Change the contrast phase, then drag to reveal lesion masks.
Region masksEarly contrastexample
Actionable insights / Clinical decision support
The whole case.
Every region.
Prioritize findings and uncertainty for clinical review.
Structured reportingA clear handover
A clear handover
from AI to clinician.
Link image evidence to review priorities and follow-up actions.
- Case summary and review priorities
- Lesion measurements and enhancement
- Quality control and uncertainty
- Contour edits, notes and sign-off
NEVYON DATALABS
CASE DCE-001
Awaiting human reviewBreast DCE-MRI
Contrast enhancementL1: washout · L2: progressive
Patient-level overview
- Study
- Breast DCE-MRI · 4 contrast phases
- Lesions
- 2 regions · L1 and L2
- Image quality
- Passed · Mild motion
- Boundary uncertainty
- L1: moderate · L2: low
- Review priority
- Check L1 margins before sign-off
Reviewer note
Inspect L1 across phases. Accept or edit the contours, then confirm the report.
Specialist review & approvalPending
Explore self-training and confidence filtering
Confidence is a filter.
Validation is essential.
Use filtered teacher predictions to train a student model. Check calibration and domain shift on independent data.
Physiological signals & medical time series
Signals change.
Decisions need context.
Preprocess→Classify & reconstruct→Forecast→Clinical review
Interactive / Synthetic signal
FHR-like pattern · NormalizedStart with the observed data.
ObservedReconstructionForecastIllustrative range
example · Signal curves and uncertainty ranges are illustrative.
Learn from incomplete signals.
Masked transformers for reconstruction, inpainting and temporal learning.
Evaluate what can be trusted.
Test uncertainty across recording conditions, missing data and forecast horizons.
Keep clinicians in control.
Traceable outputs and quality flags, with observed and estimated data clearly separated.
Engineering, data science & applied research
A full engineering
perspective.
Connect data preparation and model development with dependable software delivery.
Build the workflow
around the model.
Build low-latency inference services with versioned data, CI/CD and monitoring.
Python · PyTorch · Docker · CI/CD · Model monitoring
Discuss the support your team needs ↗ML delivery workflowIllustrative architecture
01Data & versions
02Train & evaluate
03Package & test
04CI/CD
05Deploy & monitor
The research behind the approach
Built on published work.
Across images, text and signals.
Selected research co-authored by Neel, with links to the original methods and results.
Explore the full Google Scholar profile ↗Explore selected research
Image quality
Artifact processing
at pipeline scale.
Mixture-of-experts artifact detection and the trade-offs between computation and performance.
Read the published study ↗Clinical note
→Summary
Finding the signal
in clinical notes.
Attention-based extractive summarization to identify meaningful phrases in clinical text.
Read the research ↗Signal reconstruction
Working with
gaps in a signal.
A self-supervised transformer approach to fetal heart rate inpainting and forecasting.
Read the preprint ↗Privacy-aware computing
Keep data constraints
in the design.
Privacy, budget and deadline considerations for large medical image processing across hybrid clouds.
Read the preprint ↗Card artwork is explanatory. Publication links contain the original methods and results.
How we work
A clear scope.
A working handover.
Work directly with a specialist throughout the project.
01
Understand & scope
Agree the data, deliverables and success criteria.
Outcome / A practical project plan02
Develop & evaluate
Build in stages, evaluate performance and review the results.
Outcome / Working code and evidence03
Deliver & hand over
Deliver code, documentation and support for integration.
Outcome / A result your team can use
Neel Kanwal, Ph.D.Founder & CEO
About Nevyon
AI expertise.
Worldwide collaboration.
Neel Kanwal, Ph.D., Founder & CEO, leads Nevyon DataLabs’ AI engineering and data science work.
His expertise spans computational pathology, medical imaging, physiological signals and clinical NLP, from model development to software delivery.
Headquartered in Oslo, Norway. Available for remote projects and collaborations worldwide.
Computer visionClinical NLPApplied researchML engineering
Let’s work on it
What does your
team need to
move forward?
Tell us what your team needs to build or improve.
nevyondatalabs@gmail.com ↗Remote collaboration worldwideFollow Nevyon ↗