Breast Cancer Computer-Aided Detection

QuData's AI-Driven Solution for Accurate Cancer Diagnosis

Empower Your Medical Practice with Advanced Technology

QuData's AI-powered system enhances the accuracy of breast cancer detection, reducing the likelihood of missed diagnoses and false positives. This enables early detection and intervention while providing a reliable second expert opinion for medical practitioners. Our approach also achieves cost-efficiency by minimizing unnecessary medical procedures and reducing associated healthcare costs.

Key Features: What Makes Our Solution Stand Out

Enhanced Diagnostic Accuracy
  • Offers unparalleled precision in breast cancer detection.
  • Reduces the likelihood of missed diagnoses and false positives.
Early Detection and Intervention
  • Identifies breast cancer at its earliest stages, leading to more effective treatments.
  • Increases the chances of full recovery and improved patient outcomes.
Second Expert Opinion
  • Serves as a reliable second expert opinion for medical practitioners.
  • Provides additional insights and confirmation in complex or challenging cases.
Educational Platform
  • Empowers medical students and professionals to enhance their diagnostic skills.
  • Offers a learning environment with access to real-world medical cases and expert guidance.
  • Minimizes unnecessary medical procedures and treatments.
  • Reduces healthcare costs associated with diagnostic testing

From Data to Diagnosis: How it Works

Each mammogram image automatically is put through the AI system. A scalable architecture allows for expanding external requests without limitations on the number of requests from users.

Mammography is performed in clinics or hospital x-ray departments. Our model excels in various clinical environments and is resilient to minor equipment modifications.

Lesion areas are marked by bounding boxes. Class activation maps are used to detect various abnormalities such as calcifications, mass, and other critical indicators.

Breast Imaging-Reporting and Data System or BI-RADS classification is assigned by the AI system for each mammogram view. It uses advanced neural networks, including CNNs, for medical image analysis.

Making a Difference:
State-of-the-art Accuracy

We have achieved a significant accuracy level of 0.8 for the F1‑score. This metric combines precision and recall into a single measure. The F1‑score is calculated as the harmonic mean when evaluating the 5‑class BI-RADS level classification and the 4‑class Density classification.