ICVRL International Computer Vision Research Laboratory
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Core Research Specialization

Medical Image Analysis

AI-driven analysis of medical images including X-rays, MRI, CT scans for automated diagnosis and treatment planning.

1 Researchers 2 Projects 3 Publications
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In-Depth Overview

Research Focus & Scope

Medical Image Analysis is a core pillar of ICVRL's translational research mission. We collaborate with healthcare institutions and clinical experts to develop AI-assisted diagnostic tools that interpret X-rays, MRI scans, CT scans, ultrasound, and histopathology slides. Our primary breakthroughs include early COVID-19 screening, breast cancer detection, drowning pathology diagnosis, and automated tumor segmentation.
Methodological Approach

How We Conduct Research in Medical Image Analysis

We utilize deep convolutional networks, 3D UNet architectures, spatial transformers, and explainable AI (XAI) frameworks (such as Grad-CAM and integrated gradients) ensuring that clinical predictions are fully interpretable, trustworthy, and actionable for medical professionals.
Impact & Deployments

Real-World Applications

Automated early-stage oncology and tumor detection
Pulmonary lesion screening and pneumonia classification
Real-time ultrasound guidance and cardiac imaging analysis
Clinical workflow acceleration and diagnostic triage
Pathology slide digital quantification
Lab Projects

Projects in this Domain

All Projects

MedSeg-AI: Intelligent Medical Image Segmentation Platform

An AI-powered platform for automated segmentation of medical images including CT, MRI, and X-ray scans with cl...

active Code

SynMed: Synthetic Medical Data Generation Framework

A GAN-based framework for generating high-fidelity synthetic medical images to address data scarcity in health...

completed Code
Scholarly Output

Recent Publications

All Publications
journal

A Comprehensive Survey of Vision Transformers in Medical Imaging

Shams ur Rehman, Muhammad Hamza, Ayesha Malik, Ahmad Khan

ACM Computing Surveys, 2025

journal

Deep Attention-Guided Network for Medical Image Segmentation

Shams ur Rehman, Ahmad Khan, Muhammad Hamza

IEEE Transactions on Medical Imaging, 2025

journal

GAN-Based Synthetic Data Augmentation for Rare Disease Classification

Muhammad Hamza, Shams ur Rehman, Ayesha Malik

Medical Image Analysis, 2024

Tech Stack & Tools

3D UNet MONAI PyTorch SimpleITK ITK-SNAP DICOM Processing Grad-CAM Captum OpenCV

Active Researchers

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