Building trustworthy AI systems for medical image analysis and clinical decision support.

I am Amir Reza Naderi, a PhD researcher in Biomedical AI with a background in Biomedical Engineering and research experience in machine learning, computer vision, and medical image analysis.

My work focuses on developing deep learning methods for clinically relevant imaging problems, with particular interest in medical image classification, segmentation, biomarker discovery, and explainable AI.

• 🔬 Researching deep learning for biomedical image analysis
• 🧠 Interested in medical AI, computer vision, ophthalmology, and explainability
• 🛠 Daily tools: Python, PyTorch, MONAI, OpenCV, scikit-learn, MATLAB
• 📚 Publications available on Google Scholar
• 🤝 Open to research collaborations and academic partnerships
• 📫 Reach me at naderi.bme@gmail.com

• Deep learning for glaucoma detection from fundus images
• Optic nerve head and retinal structure analysis
• Explainable AI for medical imaging
• Deep learning-based biomarker discovery
• Transfer learning for limited medical datasets
• Clinically interpretable AI systems

Development of deep learning pipelines for glaucoma detection using fundus images, including optic nerve head analysis, image classification, segmentation, and saliency-based explainability.

Keywords: Fundus Imaging · Glaucoma · MONAI · PyTorch · Explainable AI

Machine learning and deep learning-based classification of non-alcoholic fatty liver disease using ultrasound images, radiomics, convolutional features, and classical machine learning classifiers.

Keywords: NAFLD · Ultrasound Imaging · Transfer Learning · Radiomics · CNNs

Research interest in developing interpretable models for medical image analysis, focusing on understanding what deep learning models learn from clinical images.

Keywords: Grad-CAM · Saliency Maps · Biomarkers · Trustworthy AI

• Automatic classification of Non-alcoholic Fatty Liver using texture features from ultrasound images
  Tehran University Medical Journal, 2021

• Classifying Nonalcoholic Fatty Liver Grades using Pretrained Convolutional Neural Networks and Random Forest Classifier
  Research work on transfer learning and medical image classification

• Classification of Nonalcoholic Fatty Liver using the Integration of Textural and Convolutional Features and XGBoost Classifier
  Research work on hybrid feature extraction and machine learning

For the full publication list, please visit my
Google Scholar Profile.

Python · PyTorch · TensorFlow · Keras · MONAI · OpenCV · scikit-learn · MATLAB · NumPy · Pandas

Deep Learning · Transfer Learning · CNNs · Image Classification · Segmentation · Feature Selection · Radiomics

Fundus Imaging · Ultrasound Imaging · Biomedical Image Analysis · Clinical AI · Explainable AI

I am interested in collaborations related to:

• Medical image analysis
• Ophthalmic AI
• Explainable deep learning
• Biomedical computer vision
• AI-based clinical decision support
• Research software development in Python and PyTorch

Thanks for visiting my profile. Always open to meaningful research collaboration and new scientific ideas.