Research Publications

The study outlines the development of an autonomous AI system for chest X-ray (CXR) interpretation, trained on a vast dataset of over 5 million X-rays sourced from healthcare systems across India. This AI system integrates advanced architectures, including Vision Transformers, Faster R-CNN, and various U-Net models (such as Attention U-Net, U-Net++, and Dense U-Net), to enable comprehensive classification, detection, and segmentation of 75 distinct pathologies.

This study introduces a Vision-Language Model (VLM) leveraging SIGLIP and Gemma-3b architectures for automated acute tuberculosis (TB) screening. By integrating chest X-ray images and clinical notes, the model aims to enhance diagnostic accuracy and efficiency, particularly in resource-limited settings.

A comprehensive deep learning approach for automated MRI spine pathology detection, designed to work effectively across diverse clinical settings and patient populations.

Exploration of cutting-edge deep learning methodologies for enhanced medical image analysis, focusing on improved diagnostic accuracy and clinical workflow optimization.

Development of multimodal AI systems that integrate multiple imaging modalities and clinical data for comprehensive medical analysis and improved diagnostic outcomes.

Novel machine learning approaches designed to optimize radiology workflows, reduce reporting times, and enhance overall efficiency in clinical practice.

Advanced deep learning models specifically designed for early disease detection in medical imaging, enabling proactive healthcare interventions and improved patient outcomes.

Development of AI-powered clinical decision support systems that assist radiologists in making more accurate diagnoses and treatment recommendations.

Cutting-edge AI technologies and methodologies for the next generation of medical imaging systems, focusing on scalability, accuracy, and clinical integration.