I provide PhD-level expertise in developing custom Deep Learning architectures for medical image analysis. With a specialized focus on neuroimaging and neurodegenerative disorders, I help research labs and healthcare startups transform raw DICOM/NIfTI data into actionable clinical metrics.

Core Capabilities:

Precision Segmentation: High-accuracy (90%+) models using 3D UNet, Fusion-Net, and Vision Transformers (ViT).

Neuroimaging Expertise: Specialized pipelines for Multiple Sclerosis (MS) detection and brain lesion analysis.

Preprocessing Pipelines: Advanced skull stripping, intensity normalization, and multi-modal registration (T1, T2, FLAIR).

Scientific Validation: Model evaluation using clinically meaningful metrics and support for research publication.

Deliverable: Fully trained PyTorch/TensorFlow models, documented preprocessing scripts, and validation reports.