About Me

A Python library for easy access to 2D and 3D Medical Image Segmentation datasets provided in varying resolutions and sizes, designed for benchmarking and learning purposes.

Multimodal learning framework to predict 5-year survivability and recurrence rates. Integrated whole-slide image features from CLAM with structured clinical data, laboratory analytes, surgical notes, and patient history. Used ClinicalBERT for text embeddings and low-rank bilinear fusion, achieving an AUC of 0.75 across tasks.

End-to-end system integrating surgical video data and panoptic segmentations for automated operating room scene understanding. Trained S2-Scaled SwinUNETR for panoptic segmentation across 21 classes. Fine-tuned MedGemma using QLoRA for scene graph generation and event significance detection.

Accessible AI assistant built on Large Language Models with Retrieval-Augmented Generation to help expectant mothers with gestational diabetes. Deployed through WhatsApp for inclusivity and ease of access, providing personalized health guidance in low-resource settings.

Developed generative models to improve medical imaging quality and data efficiency. Super-resolution and denoising framework for high-resolution head CT scans from low-resolution variants, reducing radiation exposure. Implemented Deep Convolutional GANs for synthetic medical image generation for self-supervised pre-training and data-efficient fine-tuning.