AcuVizionAI builds V7-CORE++, a production-grade core that powers explainable diagnostics, AI-assisted drug discovery and no-leak fraud pipelines – all from the same calibrated, auditable framework.
[health.v7] run_pipeline --task derm_multi --no_leak strict ✓ Loading multi-modal inputs • dermoscopic image • clinical image • structured metadata ✓ V7-CORE++ encoder • shared backbone for vision + tabular • calibrated head (ECE < 2.5%) • XAI hooks: Grad-CAM++ & SHAP ✓ Safety checks • patient-level split: strict • timestamp leakage: none • audit log: /logs/derm/ISIC2025.json → Output • malignancy risk: 0.83 • triage: "biopsy recommended" • explanation: focus on lesion, not artefacts
V7-CORE++ is a reusable AI core that can see, read and reason over medical images, structured records, molecules and financial signals – without rewriting the engine for every new project.
Multi-modal models for skin cancer and dermatology, combining dermoscopic & clinical images with metadata. No-leak splits are enforced at patient level, and every prediction ships with Grad-CAM++ and SHAP-based explanations to support clinical decisions.
V7-CORE++ powers a generative-plus-screening pipeline for extremely hard targets such as c-Myc, KRAS and mHTT. The system proposes synthesizable molecules, filters them through ADMET and off-target predictors, and explains why each fragment matters for binding.
Time-aware, group-exclusive splits, point-in-time corrections and full audit logs are built into the fraud and tax-risk pipelines. V7-CORE++ delivers calibrated scores, interpretable drivers and documentation aligned with regulators and risk committees.
V7-CORE++ was built around one idea: models are only useful if experts can trust, audit and challenge them.
Share a few details and we’ll send a short PDF explaining how V7-CORE++ could align with your use case (no marketing fluff, just architecture, safeguards and example metrics).
V7-CORE++ is being hardened across multiple domains, with a focus on reproducible experiments, clean benchmarks and real-world readiness.
Clean evaluation on public dermatology benchmarks with patient-level splits, multi-modal fusion (image + metadata) and post-hoc bias analysis. Designed to be extended into clinical pilots with hospitals and research labs.
Docking-aware generative loops, fragment-level explanations and strict synthesizability filters have produced focused hit lists for c-Myc, KRAS and HTT. These are explicitly designed for wet-lab validation with partners, not as “magic black-box hit factories”.
An experimental module exploring digital scent representations and AI-assisted candidate generation for specific olfactory families (e.g. amber, sandalwood). Intended as a frontier R&D track on top of the same V7-CORE++ engine.
If you’re working on a concrete dataset or a high-stakes use case and want to explore whether V7-CORE++ can help, reach out with a short description. We can start with a small, clearly scoped experiment.
AcuVizionAI is based in Belgium and operates as a lean deep-tech studio: engineering-heavy, research-driven and obsessed with data hygiene and explainability.
Deployment can range from research notebooks to fully containerized services that run on your own infrastructure or VPS.
For now, use e-mail or LinkedIn to get in touch. This can easily be wired to your own backend form handler on the VPS.
E-mail: [email protected]
Location: Belgium (Flanders)