
Radwa
Computer Vision Medical AI Engineer Deep Learning Specialist
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Computer Vision Engineer | Medical Imaging Deep Learning Built cross-modal segmentation using Swin Transformer for multi-modality generalization. Objectives: Generalization across modalities, improving imbalanced segmentation, stable efficient training. Solution: Custom SwinSegAblationCustom with Hybrid Cross Attention, FFT Dynamic Frequency Analyzer, Multi-Head Attention in modular ablation-ready design. Training: BCEWithLogits + Dice, AdamW lr 1e-4 wd 1e-5, cosine annealing 25 epochs, AMP, gradient clipping. Data: 80/20 split, 224×224 resizing, flips/rotations/brightness/contrast/saturation augmentations. Metrics: Dice, IoU, Precision, Recall, Cohen’s Kappa (accuracy not used due to imbalance). Improvements: Stabilized FFT (float32, no autocast), safer GPU ops, gradient clipping, stronger augmentation. Results: Improved Dice/IoU with stable convergence. Innovation: Combines spatial + frequency learning with modular experimentation and efficient training. Impact: Scalable cross-modal segmentation for research and clinical use. Stack: Python, PyTorch, Swin Transformer | 5.0 client rating.
Upwork • Freelance
Dec 2021 - Dec 2025 • 4 yrs
skilled at machine learning and deep learning specialization, build model with different algorithms and visualize the results