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OncoVision AI v2.5 Enterprise
AI API: Online

Multimodal Breast Cancer Diagnostic Studio

Integrated AI Pipeline combining MedCLIP Vision Transformers, TabNet Deep Clinical Classifier, ResNet Modality Auto-Routing, and Radiomics Texture Feature Engine.

⚡ Auto Modality Classifier (100% Acc) 🔬 MedCLIP 384x384 Specialist 📊 TabNet Clinical Fusion 📐 Radiomic Shape & Intensity Analysis
🚀 Quick Sample Cases: Load verified medical test presets to evaluate the pipeline in 1-click.

1. Image Diagnostic Studio

Auto-Detect Active
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Drag & Drop Scan Here or click to browse

Supports DICOM, PNG, JPEG, TIFF (USG / Mammogram)

2. Clinical & Biomarker Matrix

TabNet 9-Feature Schema
Primary demographic factor in incidence weighting.
Standard ACR Breast Imaging Reporting System classification.
Geometric mass contour configuration.
Border definition between mass and parenchymal tissue.
Background parenchymal density or acoustic impedance.
Mineral deposits identified on mammography.
Maximum diameter in millimeters measured on scan.
Genetic predisposition risk factor.
Previous histological evaluation history.

📷 Live Ultrasound/Scan Camera Capture

📁 Patient Case History Log

Date Age Modality Diagnosis Conf BI-RADS
No patient records saved yet.

📖 ACR BI-RADS & Radiomics Educational Reference

Breast Imaging Reporting and Data System (BI-RADS)

  • BI-RADS 0: Incomplete assessment. Additional imaging needed.
  • BI-RADS 1: Negative. Symmetrical, normal tissue architecture.
  • BI-RADS 2: Benign finding (e.g. Simple fluid-filled cyst, fibroadenoma).
  • BI-RADS 3: Probably benign finding (< 2% risk of malignancy). Short-interval follow-up.
  • BI-RADS 4: Suspicious abnormality (2% to 95% malignancy risk). Core biopsy recommended.
  • BI-RADS 5: Highly suggestive of malignancy (≥ 95% risk). Immediate biopsy & oncology consult.
  • BI-RADS 6: Known biopsy-proven malignancy. Surgical planning.

Multimodal AI Architecture

This system leverages late fusion of vision transformers (MedCLIP) and tabular decision trees (TabNet). Image features are combined with 9 clinical parameters and 6 radiomic shape/texture descriptors to maximize screening sensitivity and specificity.

⚠️ Clinical Research Disclaimer: OncoVision AI is designed for educational and research purposes only. It is not intended for independent diagnostic reliance. All predictions must be validated by a licensed Radiologist or Medical Institution.