MEDICAL AI CASE STUDY
Advancing Breast Cancer Detection with AI
Multi-model deep learning solution combining CNN classification and U-Net segmentation for improved diagnostic accuracy
- Cloud Native
- Generative AI
- Edge Computing
- LLMs
Breast Cancer Detection Using CNN and U-Net Models
Multi-model deep learning solution combining CNN classification and U-Net segmentation for improved diagnostic accuracy
- Strategy-led delivery
- AI, cloud, web, and automation expertise
- Secure engineering practices
The Diagnostic Challenge
- Strategy-led delivery
- AI, cloud, web, and automation expertise
- Secure engineering practices
Our AI-Powered Solution
We developed a comprehensive deep learning system that combines multiple neural network architectures to address the complexities of breast cancer detection: 1. Dual-Model Architecture: A CNN classifier for identifying suspicious regions and a U-Net model for precise segmentation of detected anomalies. 2. Automatic Labeling System: A proprietary framework that learns from expert annotations and generates high-quality training data, reducing the manual labeling burden. 3. Multi-Modal Integration: Capability to process various imaging formats including mammograms, ultrasounds, and MRIs for comprehensive analysis. 4. Explainable AI Components: Visualization tools that highlight the regions influencing the model’s decisions, building trust with medical professionals. The system was trained on diverse, multi-institutional datasets to ensure robustness across different patient demographics and imaging equipment.
- CNN Classification: Advanced convolutional networks identify suspicious regions with high accuracy
- U-Net Segmentation: Precise boundary detection for identified anomalies
- Automatic Labeling: AI-assisted annotation reducing manual effort
- Multi-Modal Support: Compatibility with various imaging formats
- Explainable AI: Visual explanations for model decisions
- Continuous Learning: System improves with new data and expert feedback
DELIVERY PROCESS
Development Process
A structured delivery model ensuring reliability, visibility, and controlled rollouts.
Data Acquisition & Annotation
Collaborated with medical institutions to collect diverse, de-identified imaging data with expert annotations
Explore stepModel Architecture Design
Designed and tested multiple CNN and U-Net variants to optimize for medical imaging characteristics
Explore stepTraining & Validation
Implemented rigorous k-fold cross-validation and expert-in-the-loop feedback systems
Explore stepClinical Integration
Developed DICOM-compatible interfaces for seamless integration with existing hospital systems
Explore stepMEASURABLE IMPACT
Measurable Impact
The implementation of our breast cancer detection system delivered significant improvements in diagnostic accuracy and efficiency:
Overall Accuracy
Surpassing human baseline performance of 92.4%
Segmentation Dice Score
Precise boundary detection for treatment planning
False Positive Reduction
Fewer unnecessary biopsies and patient anxiety
Faster Analysis
Reduced radiologist interpretation time
“Pyzen’s AI system has transformed our breast imaging practice. It serves as a consistent second reader that never gets tired, helping us catch subtle findings that might otherwise be missed during high-volume reading sessions.”
Dr. Emily Rodriguez — Chief of Radiology, Metropolitan Medical Center
VERIFIED REVIEWS
Rated by Real Clients on Clutch
CASE STUDY FAQ
Frequently Asked Questions
Direct answers about this case study and implementation.
Talk to Pyzen experts for project-specific answers, architecture guidance, and delivery planning.
Discuss Your Requirements01 How does the AI system integrate with existing hospital workflows?
Our solution is designed as a DICOM-compatible assistant that integrates seamlessly with existing PACS and RIS systems. It can function as a second reader, providing analysis without disrupting established workflows.
02 What validation has the system undergone?
The system has been validated through extensive retrospective studies across multiple institutions with diverse patient demographics. We followed rigorous testing protocols consistent with FDA guidelines for AI-based medical devices.
03 How does the automatic labeling system work?
Our proprietary automatic labeling combines expert annotations with model predictions in an active learning framework. The system identifies cases where model confidence is low and prioritizes them for expert review, continuously improving its training data quality.
04 Can the system explain its decisions to clinicians?
Yes, we've incorporated explainable AI techniques that generate visual heatmaps showing which regions of the image most influenced the model's decision. This builds trust and allows radiologists to validate the AI's findings.
