Amirreza Fateh - Ph.D. Candidate in AI & Robotics

Welcome to my academic homepage!

I am a Ph.D. candidate in Artificial Intelligence and Robotics at the Iran University of Science and Technology (IUST).

My research lies at the intersection of Computer Vision and Medical Imaging, with a specific focus on making deep learning models more efficient and adaptable in real-world scenarios. I am particularly interested in developing lightweight adapters, prompt engineering strategies for foundation models (such as the Segment Anything Model - SAM), and advancing few-shot semantic segmentation techniques for clinical and remote sensing applications.

🏆 Honors & Awards

• Member, Iran’s National Elites Foundation (2022 – Present)
• Recognized as an Academically Talented Student by Iran’s National Elites Foundation for exceptional academic achievement (2022 – 2025)
• Ranked 1st in the Ph.D. in Computer Engineering coursework phase (2023)
• Ranked 1st in the M.Sc. in Computer Engineering program (2021)
• Ranked 2nd in the B.Sc. in Computer Engineering program (2019)

🔬 Research Interests

• Computer Vision: Few-Shot & One-Shot Semantic Segmentation, Multi-Scale Feature Extraction
• Medical Image Analysis: Brain Tumor Segmentation, Disease Classification, Lightweight Box Predictors
• Foundation Models: Adapters, Prompt Learning (MedSAM), Parameter-Efficient Fine-Tuning (PEFT)
• Document Analysis: OCR, Layout Analysis, Multilingual Handwriting Recognition

📢 Recent News

• [Early 2026] Our paper “Adapting SAM with a Triple-Prompt Strategy for One-Shot Semantic Segmentation” was published in Neurocomputing.
• [Early 2026] Released the BRISC dataset and the Swin-HAFNet architecture for Brain Tumor Segmentation and Classification. We are thrilled that the dataset has already reached nearly 10,000 downloads!
• [2022 - Present] Continuing my role as Lab Coordinator, where I mentor M.Sc. and Bachelor’s students in their thesis projects.

🤝 Academic Service

I am deeply committed to the scientific community and have completed over 300 peer reviews for top-tier international journals. Selected journals I review for include:

• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
• IEEE Transactions on Image Processing (TIP)
• IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
• Information Fusion
• Expert Systems with Applications
• Pattern Recognition