Computer Vision Engineering
Production computer vision systems built with modern architectures and rigorous evaluation. We deliver medical imaging analysis, biometric verification, and visual inspection systems that perform reliably in operational environments.
Computer Vision Capabilities
Medical Image Classification
Deep learning for diagnostic imaging including brain MRI analysis, cell classification, and pathology image processing. Published work achieving 99.55% accuracy with proper calibration and cross-dataset validation.
Face Liveness Detection
Production biometric verification systems. Our face liveness detection application verified more than 40,000 pensioners in a government identity program, handling real-world conditions and diverse populations.
Vision Transformers
Implementation and fine-tuning of Swin Transformer, DeiT, and hybrid vision architectures. Expertise in feature extraction, attention mechanism analysis, and combining transformers with classical ML methods.
Model Calibration
Ensuring vision models provide reliable uncertainty estimates. Critical for medical applications where knowing prediction confidence affects clinical decisions. Our Frontiers in Neuroscience paper addresses calibration-aware classification.
Explainability & Visualization
Grad-CAM, attention map visualization, and feature importance analysis for understanding model decisions. Essential for regulated domains and building trust in AI systems.
Mobile & Edge Deployment
Optimize vision models for mobile devices and edge environments. Experience deploying face verification on mobile platforms with performance constraints and offline operation requirements.
Demonstrated Results
- Brain tumor MRI classification: 99.55% accuracy (DeiT + SVM hybrid pipeline)
- Published calibration-aware CNN-Swin model for cross-dataset brain MRI (Frontiers in Neuroscience 2026)
- Face liveness detection: 40,000+ successful verifications in government program
- Federated learning research on cervical cell classification with privacy preservation
- Multi-seed evaluation protocols ensuring reproducible computer vision results
Frequently Asked Questions
- What computer vision applications does Kortex8Labs develop?
- We develop medical image classification systems, face liveness detection, identity verification workflows, visual inspection systems, and custom image analysis pipelines for production use.
- What architectures does Kortex8Labs use for computer vision?
- We work with vision transformers (Swin, DeiT), convolutional neural networks (ResNet, EfficientNet), hybrid architectures combining transformers with traditional ML (SVM), and custom pipelines optimized for specific domains.
- Can Kortex8Labs handle medical imaging projects?
- Yes. We have published research on brain tumor MRI classification achieving 99.55% accuracy, federated learning for cervical cell classification, and experience with cross-dataset generalization and model calibration for clinical applications.
- How does Kortex8Labs validate computer vision models?
- We use multi-seed evaluation for statistical confidence, calibration analysis to quantify prediction uncertainty, explainability methods (Grad-CAM, attention visualization), and cross-dataset testing to ensure generalization beyond training data.
- What data volumes are needed for computer vision projects?
- Requirements vary by task complexity. We work with transfer learning, few-shot learning, and data augmentation to maximize value from available data. For sensitive domains, we implement federated learning to train across distributed datasets without centralization.
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Share your vision challenge, data characteristics, and deployment environment. We'll propose a technical approach grounded in what has worked in similar contexts.
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