SwiftDx is an AI-powered pathology platform that virtually stains tissue samples and performs real-time cancer margin assessment using MONAI-based medical imaging models and large vision-language architectures.
F1 Margin Accuracy
Workflow Reduction
Residual MONAI Units
Lower Diagnostic Cost
Traditional histopathology requires fixation, sectioning, staining, and manual review before a tumour margin can be verified.
Delayed tumour boundary assessment increases re-excision rates and creates unnecessary patient trauma and healthcare cost.
Cancer case volume is growing faster than pathology interpretation capacity across global healthcare systems.
Immunohistochemistry workflows add additional cost and turnaround time when H&E interpretation becomes ambiguous.
Surgical tissue is digitised through fluorescence and bright-field imaging systems.
Reinforcement-learning guided models digitally reconstruct histological staining while preserving nuclei and tumour morphology.
MONAI-based LVLM architectures analyse tumour boundaries and classify margins with confidence overlays.
Structural similarity metrics validate generated pathology against real H&E stained tissue.
Reinforcement learning improves histological realism and tissue consistency.
Medical imaging framework enabling scalable pathology inference.
Large vision-language medical models provide contextual tumour interpretation.
SwiftDx combines virtual staining, digital pathology, reinforcement learning, and AI-assisted tumour analysis into a single surgical intelligence platform.