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.

Recognized for its impact, this project received a grant from the Seeds of Bravery program to support further development of AI in diagnostics.

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.

Qudata's Solution Wins EU Grant

Computer-Aided Technology for Early Breast Cancer Detection by QuData was selected for the Seeds of Bravery program, funded by the European Union under the European Innovation Council (EIC). As part of the UASEEDs Deep Tech Scale-up and Acceleration Open Call, QuData’s technology received a grant to support its development.

Our project has been recognized with the Seal of Excellence under the UASEEDs grant program. This prestigious badge highlights QuData’s commitment to innovation and the potential impact of our AI-driven technology to enhance early breast cancer detection and improve patient outcomes.

Description

Technical Details: Integration & Security

Access detailed technical specifications and security protocols for Breast Cancer Computer-Aided Detection by QuData. Learn how to seamlessly integrate the QuData’s service for AI-powered mammography analysis with PACS systems to enhance medical image processing while ensuring security and efficiency.

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Frequently Asked Questions

What is QuData’s breast cancer computer-aided detection service?

It is an AI-powered service from QuData’s QuMed initiative that analyzes mammograms and assigns a BI-RADS category to each image. It helps radiologists detect breast abnormalities earlier, reduces missed diagnoses and false positives, and provides a second expert opinion.

What is QuMed, and how is it related to QuData?

QuData is an AI and machine learning company. QuMed is QuData’s dedicated initiative for applying artificial intelligence in medicine. It develops AI-driven solutions for medical image analysis, diagnostic support and workflow optimization in healthcare institutions, including the breast cancer computer-aided detection service described on this page.

How does AI mammography analysis work?

Mammograms taken in a clinic or hospital are sent to the AI system, which preprocesses and segments the image and classifies it with convolutional neural networks. The system assigns a BI-RADS category to each view and marks suspicious areas with bounding boxes and class activation maps (Grad-CAM). These highlight findings such as masses and calcifications, and the result is returned as a structured report for the radiologist to review.

What is BI-RADS, and which categories does the AI assign?

BI-RADS (Breast Imaging-Reporting and Data System) is the standard scale for reporting mammography findings. The model classifies images into BI-RADS 1 (negative), 2 (benign findings), 3 (probably benign), 4 (suspicious abnormality) and 5 (highly suggestive of malignancy). It also classifies breast density into 4 classes.

How accurate is QuData’s breast cancer detection system?

The current model reaches an overall accuracy of more than 80%.

Can the AI replace radiologists?

No. The service is a decision-support tool. It provides a second opinion, helps prioritize urgent cases, and reduces time spent on routine mammogram reviews, while the final diagnosis remains the responsibility of a qualified medical professional.

Who is the AI-powered breast cancer detection service for?

Diagnostic centers, mammography clinics, multispecialty hospitals with radiology departments, oncology centers and private practices with mammography equipment.

Radiologists use it for second opinions; junior radiologists and students use it as a training tool; and it supports telemedicine for remote or underserved regions.

Can QuData’s AI mammography solution integrate with existing hospital systems?

Yes. QuData’s AI-powered mammography analysis solution is designed to integrate with existing medical imaging workflows through PACS (Picture Archiving and Communication System) and the DICOM protocol (Digital Imaging and Communications in Medicine).

It works with equipment from different manufacturers. The average processing time is 10-15 seconds per image, and the system scales automatically with the number of simultaneous requests.

How is patient data protected?

Data in transit is encrypted over HTTPS and DICOM images are stored with AES-256 encryption on a secure cloud platform. After you retrieve your results, the images are deleted from QuData’s servers. If they are not retrieved, they are deleted automatically within 24 hours. The service is built to meet HIPAA and GDPR requirements.

Can I try QuData’s breast cancer detection service for free?

Yes. Sign up for a free trial on the QuMed platform to upload mammograms and see BI-RADS results with highlighted findings. For integration and security details, see the service documentation.