Medical Imaging: Cancer & Disease Detection

Challenge

Radiologists face overwhelming workloads with increasing imaging volumes, leading to diagnostic fatigue, delayed reporting, and missed findings. RadSupport needed AI systems that could serve as a reliable second reader — matching radiologist-level accuracy across multiple imaging modalities.

Solution

Breast Cancer Detection from Mammograms

Designed and built an end-to-end deep learning pipeline for detecting malignant lesions in mammographic images. The system leveraged convolutional neural networks trained on large-scale annotated datasets, incorporating multi-view analysis (CC and MLO projections) and region-of-interest localization to flag suspicious masses and microcalcifications with high sensitivity.

Multi-Pathology Chest X-Ray Classification

Engineered a multi-label classification system capable of identifying 18 distinct pathologies from chest radiographs — including pneumonia, pleural effusion, cardiomegaly, pneumothorax, and atelectasis. Built a custom training pipeline with class-imbalance handling, attention mechanisms, and ensemble techniques to achieve production-grade diagnostic performance.

Results

• Achieved radiologist-grade accuracy across both mammography and chest X-ray pipelines
• Detected 18 distinct pathologies from chest radiographs in a single inference pass
• Reduced average reporting turnaround time for preliminary reads
• Designed the system for seamless integration into existing radiology workflows

Key Insight

Medical imaging AI isn't just about model accuracy — it's about building systems that radiologists trust. Every design decision, from confidence calibration to explainable heatmaps, was made with clinical adoption in mind.