Publications
Research Vision & Purpose
My research is driven by the goal of bridging the gap between complex deep learning architectures and highly practical, real-world applications. Specifically, I focus on developing parameter-efficient models—such as lightweight adapters and few-shot segmentation frameworks—that maintain state-of-the-art accuracy even when annotated data is scarce. By pushing the boundaries of Medical Image Analysis, Core Computer Vision, and Document Recognition, my work aims to create robust AI tools that can be reliably deployed in clinical diagnostics and complex visual environments. Below is a categorized selection of my peer-reviewed contributions, including several published in top-tier (Q1) international journals.
My research is driven by the goal of bridging the gap between complex deep learning architectures and highly practical, real-world applications. Specifically, I focus on developing parameter-efficient models—such as lightweight adapters and few-shot segmentation frameworks—that maintain state-of-the-art accuracy even when annotated data is scarce. By pushing the boundaries of Medical Image Analysis, Core Computer Vision, and Document Recognition, my work aims to create robust AI tools that can be reliably deployed in clinical diagnostics and complex visual environments. Below is a categorized selection of my peer-reviewed contributions, including several published in top-tier (Q1) international journals.
🌟 Core Computer Vision & Foundation Models
Focusing on few-shot learning, semantic segmentation, and adapting large foundation models (like SAM) for specialized tasks.
- A. Fateh, M. Mohammadi, M. Jahed Motlagh. “Adapting SAM with a Triple-Prompt Strategy for One-Shot Semantic Segmentation.” Neurocomputing,Published 2026. Link
- F. Askari, A. Fateh, M. Mohammadi. “Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms.” Neural Networks,Published 2025. Link
- A. Fateh, M. Mohammadi, M. Jahed Motlagh. “MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping.” Image and Vision Computing,Published 2025. Link
- A. Saber, M. Hosseini, A. Fateh, M. Fateh, V. Abolghasemi. “Lightweight Multi-Scale Framework for Human Pose and Action Classification.” Sensors, Published 2026. Link
- M. Gholami, M. Fateh, A. Fateh. “Multi-Modal Semantic Image Segmentation Model Based on Improved DeepLabV3+ Framework Using Attention Modules.” International Journal of Engineering (IJE), Published 2026. Link
🏥 Medical Image Analysis & Diagnostics
Applying advanced multi-scale refinement and segmentation to critical healthcare challenges, from brain tumors to respiratory diseases.
- A. Fateh, Y. Rezvani, S. Moayedi, S. Rezvani, F. Fateh, M. Fateh, V. Abolghasemi. “BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification.” Scientific Data, Published 2026. Link
- A. Saber, M. Sharifi Fakhim, A. Fateh, M. Fateh. “A Lightweight Multi-Scale Refinement Network for Gastrointestinal Disease Classification.” Expert Systems with Applications,Published 2026. Link
- S. Rezvani, M. Fateh, Y. Jalali, A. Fateh. “FusionLungNet: Multi-Scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation.” Biomedical Signal Processing and Control,Published 2025. Link
- A. Saber, A. Fateh, P. Parhami, A. Siahkarzadeh, M. Fateh, S. Ferdowsi. “Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach.” Sensors, Published 2025. Link
- M. Sharifi Fakhim, M. Fateh, A. Fateh, Y. Jalali. “DA-COVSGNet: Double Attentional Network for COVID Severity Grading.” International Journal of Engineering (IJE), Published 2025. Link
- H. Khajeha, M. Fateh, V. Abolghasemi, A. Fateh, M. H. Emamian, H. Hashemi, A. Fotouhi. “Diagnosis of Glaucoma Using Ensemble Learning Techniques with Optical Coherence Tomography.” Engineering Reports, Published 2025. Link
- S. Katami, M. Fateh, M. Rezvani, A. Fateh. “Retinal Fundus Image Segmentation Using Deep Learning.” Journal of Machine Vision and Image Processing, Accepted 2026.
📄 Document Analysis & OCR
Developing robust architectures for layout analysis and multilingual handwritten text recognition.
- A. Fateh, M. Fateh, V. Abolghasemi. “Multilingual Handwritten Numeral Recognition Using a Robust Deep Network Joint with Transfer Learning.” Information Sciences,Published 2021. Link
- A. Fateh, R. Tahmasbi Birgani, M. Fateh, V. Abolghasemi. “Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning.” IEEE Access, Published 2024. Link
- A. Fateh, M. Rezvani, A. Tajary, M. Fateh. “Persian Printed Text Line Detection Based on Font Size.” Multimedia Tools and Applications, Published 2023. Link
- A. Fateh, M. Fateh, V. Abolghasemi. “Enhancing Optical Character Recognition: Efficient Techniques for Document Layout Analysis and Text Line Detection.” Engineering Reports, Published 2023. Link
- A. Fateh, M. Rezvani, A. Tajary, M. Fateh. “Providing a Voting-Based Method for Combining Deep Neural Network Outputs to Layout Analysis of Printed Documents.” Journal of Machine Vision and Image Processing, Published 2022. Link