Radth builds lightweight CNN and Vision Transformer models that classify, triage, and risk-stratify medical images — from GI endoscopy to radiology, pathology, and beyond. Deployable on clinical hardware. Explainable by design.
Radth Vision AI
Live inference · 4 modalities
GI Endoscopy
Pre-malignant · Biopsy required
94.2%
confidence
Chest X-Ray
Normal · Routine follow-up
98.7%
confidence
Skin Lesion
High-Risk · Immediate referral
91.5%
confidence
Retinal Scan
Diabetic Retinopathy · Stage 2
96.1%
confidence
GradCAM Explainability
ActiveNormal
Inflam.
Pre-mal.
High-Risk
Zero Missed High-Risk
MC Dropout safety protocol
Lightweight, explainable, and clinically validated deep learning models deployable on standard hospital hardware — no GPU cluster required.
Four-class risk stratification from gastrointestinal endoscopy images — Normal, Inflammatory, Pre-malignant, High-Risk — aligned with ACG/ESGE clinical guidelines.
CNN-based analysis of chest X-rays and CT scans for pneumonia, nodule detection, pleural effusion, and COVID-19 screening with radiologist-level accuracy.
Whole-slide image analysis for cancer grading, cell segmentation, and Ki-67 proliferation index scoring — automated pathology at scale.
Skin lesion classification across 7 diagnostic categories — melanoma, basal cell carcinoma, keratosis, and more — with dermoscopy image support.
Retinal image analysis for diabetic retinopathy grading (0–4 severity), glaucoma risk screening, and age-related macular degeneration detection.
End-to-end custom model development for any medical imaging modality — from dataset curation and annotation to training, validation, and clinical deployment.
Need a model for a modality not listed? We build custom pipelines for any imaging type.
Discuss Your Imaging ProblemWe translate published deep learning research into production-grade medical imaging software — with the explainability and safety guarantees that clinical deployment demands.
We encode clinical risk asymmetry directly into the training objective — not as post-hoc thresholds.
Every prediction comes with saliency maps showing exactly what the model attended to.
Monte Carlo Dropout flags low-confidence cases for mandatory clinician review — structurally safe.
All models <10M parameters. Inference on CPU in <200ms. No GPU cluster required for deployment.
From raw clinical images to a validated, deployed model — we own the full stack. Every project follows our 5-stage pipeline built around clinical safety requirements.
Dataset & Annotation
Clinical dataset curation, class mapping to guidelines, and HIPAA-compliant annotation workflows.
Model Architecture
CNN + Vision Transformer selection optimised for your modality. Lightweight (<10M params) for clinical deployment.
Custom Loss + Training
Asymmetric loss functions (AEL-style) encode clinical cost asymmetry. Monte Carlo Dropout for uncertainty.
Explainability & Validation
GradCAM heatmaps, bootstrap CIs, and clinical validation studies — results your clinicians can trust.
Clinical Deployment
Docker/ONNX packaging, DICOM integration, HL7 FHIR support — runs on your existing hospital infrastructure.
Architecture Comparison
Results on HyperKvasir GI endoscopy benchmark
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