This repository contains a foundational study on the integration of Deep Learning and Convolutional Neural Networks (CNNs) into clinical radiology. This research served as an early exploration of transitioning from subjective, qualitative physician assessments to objective, automated diagnostic measurements.
- Computer Vision in Medicine: Analyzed CNN architectures for automated pattern recognition in X-ray and Ultrasound modalities.
- Fetal Diagnostics: Evaluated GE Healthcare’s SonoCNS AI for automating fetal brain assessment and standard plane measurements.
- Accuracy & Reproducibility: Investigated the reduction of inter-observer variability through AI-assisted quantitative radiographic reporting.
This work was completed in 2020 as a specialized seminar project on the "Rise of Robot Radiologists." It represents the first phase of my research trajectory, which has since evolved from Computer Vision (CNNs) to my current work in Transformer-based Clinical NLP and Genomic Biomarker Extraction (2026).