🎯 The Objective
To assist radio-oncologists in quickly identifying probable cancerous lumps in Contrast-Enhanced Spectral Mammography (CESM) images without immediate need for stat biopsies. Figure 1: Workflow.
🧠 Approach
- Data Processing: Utilized subtracted images to isolate potential masses.
- Architecture: Implemented Transfer Learning on shallow neural networks to maintain efficiency.
- Explainability: Integrated Saliency Heatmapping and GradCam to visualize why the model made a prediction—crucial for medical validation.
🏆 Results
Preliminary performance metrics indicate the model outperforms standard radiologist benchmarks in specific classification tasks. The results were validated in collaboration with a radio-oncologist.