Undergraduate researcher specializing in medical computer vision, volumetric neuroimaging (3D MRI), and explainable deep learning (XAI). Experienced in developing custom architectures using PyTorch, MONAI, vision backbones (Swin Transformer, ConvNeXt, EfficientNet), and gradient-based interpretability (Grad-CAM).
| Project | Highlights & Tech Stack |
|---|---|
| 3d-brain-mri-monai | Volumetric 3D MRI brain segmentation and preprocessing pipeline using MONAI, PyTorch, and NiBabel/SimpleITK for neuroimaging data. |
| skin-lesion-project | Multi-model benchmarking on dermatoscopic images evaluating ConvNeXt, Swin Transformer, and EfficientNet with PyTorch and Albumentations. |
| skin-lesion-gradcam-pytorch | Explainable AI (XAI) diagnostic tool generating Grad-CAM and Grad-CAM++ heatmaps to visualize feature attribution and clinical regions of interest. |
| Practical-Machine-Learning | Supervised and unsupervised ML workflows featuring feature engineering, model tuning, and ensemble methods (Scikit-Learn, XGBoost, LightGBM). |
- βοΈ Email: mohsinmalik1909@gmail.com
- π GitHub: github.com/cyberpunk92