Developed and validated a Siamese deep learning model to predict cancer treatment response from pre- and post-treatment CT scans (n=502). Built an end-to-end training and cross-validation pipeline using PyTorch and MONAI, collaborating closely with oncologists and radiologists to ensure clinical relevance and interpretability. Led a cross-institutional university–hospital project.

Built a multi-center deep learning system to predict cancer subtype from PET scans using set-based modeling (Transformers, Deep Sets, MIL, GNN). Achieved ROC-AUC up to 0.91 in cross-validation and improved external performance from 0.81 to 0.87 via test-time adaptation (entropy minimization). Led a fully remote university–hospital collaboration.

Developed and deployed YOLO-based object detection models for license plate detection and signature localization, achieving mAP0.5 of 0.97 and 0.94 respectively. Optimized robustness under varying lighting and quality conditions. Containerized and deployed models using Docker with real-time inference APIs in production.

Led the computer vision group in developing a non-contact heart rate estimation system from RGB video (3.02 MAE). Designed signal processing pipelines including ROI tracking, temporal filtering, and amplitude normalization. Coordinated deployment on mobile and embedded platforms for real-time inference.

Performed repair and maintenance of medical imaging systems including CT scanners, portable radiology devices, C-Arm systems, mammography units, and OPG systems.

Teaching assistant for courses in Deep Learning, Numerical Methods, Medical Image Analysis and Processing, Image Processing, and Medical Imaging Systems.

Applications of Deep Learning Algorithms for Medical Data Analysis

Worked on Medical Image Segmentation Using Deep Learning Methods

Design and implementation of brain surgery bipolar electrocoagulation simulator using haptic technology.