Research at NetraEdge

Overview

NetraEdge is an applied AI research organization focused on developing advanced machine learning and deep learning solutions for healthcare and medical imaging. Our work bridges the gap between artificial intelligence research and clinical applications, with a strong emphasis on explainability, robustness, and real-world deployment.

Research Focus Areas

1. Explainable Artificial Intelligence (XAI)

We develop interpretable AI systems for medical decision support, ensuring transparency and trust in high-stakes clinical environments. Techniques include Grad-CAM, SHAP, and LIME-based interpretability methods.

2. Computational Pathology

Our research focuses on AI-driven analysis of histopathology images for disease detection, classification, and grading.

3. Cytogenetic and Karyogram Analysis

We work on automated chromosome analysis using deep learning models for early detection of genetic and chromosomal disorders.

4. Multimodal Medical AI

We integrate imaging, clinical, and molecular data using multimodal deep learning frameworks to improve diagnostic accuracy.

Ongoing Research Projects

Automated Karyogram Analysis for Genetic Disorders

Development of a hybrid machine learning framework combining unsupervised feature learning with supervised classification for chromosome anomaly detection.

Explainable AI for Medical Imaging

Design of interpretable deep learning models for histopathology and radiology applications using attention-based and gradient-based explainability techniques.

Robust Medical AI Systems

Research on improving model generalization across diverse medical datasets and reducing domain shift in clinical environments.

Methodologies

Convolutional Neural Networks (CNNs)
Vision Transformers (ViT)
Hybrid supervised and unsupervised learning
Explainable AI techniques (Grad-CAM, SHAP, LIME)
Feature representation learning
Transfer learning for medical imaging
XAI

Research Goals

Improve early disease detection using AI
Enhance interpretability and trust in medical AI systems
Develop robust models for real-world clinical deployment
Bridge the gap between academic research and healthcare applications

Publications & Outputs

Our research contributes to peer-reviewed journals, conferences, and ongoing submissions in the fields of medical image analysis, computational pathology, and explainable AI.

Research