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

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Enhanced Diagnostic Accuracy
  • Offers unparalleled precision in breast cancer detection.
  • Reduces the likelihood of missed diagnoses and false positives.
02
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.
03
Second Expert Opinion
  • Serves as a reliable second expert opinion for medical practitioners.
  • Provides additional insights and confirmation in complex or challenging cases.
04
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.
05
Cost-Efficiency
  • Minimizes unnecessary medical procedures and treatments.
  • Reduces healthcare costs associated with diagnostic testing

From Data to Diagnosis: How it Works

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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.

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

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

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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.