112,120 chest X-rays. 10,000 retinal fundus images. Brain MRI tumor detection. Three specialized deep learning modules — trained on validated clinical datasets, deployable in any hospital infrastructure.
Medical imaging diagnosis is one of the highest-stakes bottlenecks in modern healthcare — radiologists, ophthalmologists, and neurologists are overloaded, and diagnostic delays cost lives. BAKAP DataLabs has engineered a production-ready deep learning platform that reads fundus photographs, chest X-rays, and brain MRI scans with the accuracy of a specialist.
Trained on NIH ChestX-ray14, the ODIR-5K ophthalmic dataset, and the BraTS brain tumor benchmark — three of the most authoritative clinical imaging benchmarks in existence — MedVision AI delivers multi-label pathology detection, explainable AI heatmaps, and REST API integration for any hospital system.
Each module is independently trained, validated, and deployable. Together, they cover the three most imaging-intensive specialties in hospital systems worldwide.
Multi-label classification of retinal fundus photographs. Detects four high-prevalence ocular diseases from a single bilateral eye scan, with Grad-CAM heatmap output for clinician review.
Deep learning analysis of frontal chest X-rays for pulmonary pathology detection. Trained on the NIH ChestX-ray14 benchmark — the world's largest labeled chest X-ray dataset.
Deep learning analysis of contrast-enhanced brain MRI for neuro-oncological pathology detection. Identifies and localizes tumor regions with Grad-CAM overlay — supporting neurosurgical planning and radiotherapy targeting.
From urban referral hospitals to remote screening programs — MedVision AI adapts to your environment, your workflows, and your patient volume.
Pre-screen incoming chest X-ray queues automatically. Flag high-probability pneumonia and mass cases for priority radiologist review. Reduce average read time by eliminating clear negatives from the worklist.
↑ Throughput · ↓ Turnaround timeScreen entire diabetic patient populations for retinopathy progression. OcularVision processes bilateral fundus images in under 400ms, enabling screening programs at primary care level without specialist presence.
5,000 patients/month at primary careDeploy PulmoScan on standard laptops with no GPU requirement. Run mobile tuberculosis screening programs in remote communities using portable X-ray units paired with our offline-capable inference engine.
Offline inference · CPU-deployableIntegrate MedVision AI into claims workflows via REST API. Objectively validate submitted imaging reports, detect inconsistencies between clinical notes and AI findings, and flag high-risk policyholders early.
REST API · FHIR-compatible outputAll three modules are built on transfer learning from ImageNet-pretrained backbones, fine-tuned on validated clinical datasets. Multi-label binary cross-entropy loss handles co-occurring pathologies. Grad-CAM generates explainability heatmaps for every inference. NeuroScan additionally uses a 3D U-Net segmentation head for precise tumor boundary delineation.
Every model traces its training data to peer-reviewed, publicly auditable clinical datasets. No synthetic data. No proprietary labels. Full reproducibility and scientific accountability.
The world's largest publicly available chest X-ray dataset. 112,120 frontal-view radiographs from 32,717 unique patients, annotated with 14 thoracic pathologies via validated NLP extraction from clinical reports. Label accuracy >90%.
Ocular Disease Intelligent Recognition dataset. 5,000 patients with bilateral color fundus photographs, labeled by trained human readers with quality control management. Eight diagnostic categories, real clinical settings.
Two complementary TB screening datasets for fine-tuning PulmoScan on tuberculosis detection. Montgomery County (USA) and Shenzhen Hospital (China) provide diverse patient populations and imaging conditions.
Brain Tumor Segmentation Challenge 2023 — the global benchmark for brain MRI analysis. Multi-institutional dataset with expert-annotated tumor sub-regions. Combined with TCGA-GBM for molecular subtype classification and outcome prediction.
All metrics reported on held-out test sets with patient-level splitting to prevent data leakage. Results are reproducible and benchmarked against published state-of-the-art.
Whether you're a single clinic or a national health ministry, MedVision AI has a deployment model that fits your infrastructure and budget.
Full model setup on your infrastructure + validation report on your patient data + staff training session. Delivered within 5 business days.
Tell us about your setting — hospital, screening program, insurance, or research. We'll match you to the right plan and run a live demo on your imaging data.
No commitment. We respond within one business day.