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  • Sharif University of Technology
  • Tehran, Iran
  • 04:49 (UTC +03:30)

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a-fsh-r/README.md

a-fsh-r About me:

🎓 I recently completed my Master of Science in Computer Engineering – Bioinformatics at Sharif University of Technology, where I worked as a Graduate Research Assistant during my studies.

During this time, I was involved in research at two laboratories:

  • 🩺 Biomedical Signal and Image Processing Laboratory (BiSIPL) - EE Dept.
    Department of Electrical Engineering
    I worked on biomedical image analysis using deep learning methods, with a focus on diagnostic applications.

  • 🤖 Robust and Interpretable Machine Learning Laboratory (RIML) - CE Dept.
    Department of Computer Engineering
    My work focused on interpretable and robust machine learning models, particularly for medical image analysis and computer vision tasks.

🧠 Research Interests:

  • Machine Learning / Deep Learning
  • Computer Vision
  • Image Processing (General, Medical, and Biomedical)
  • Trustworthy ML / Explainable AI (XAI)
  • AI for Healthcare

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  1. Machine_Learning Machine_Learning Public

    Forked from SharifiZarchi/Introduction_to_Machine_Learning

    Introduction to Machine Learning, for B.Sc. Students, Machine Learning for Bioinformatics for M.Sc. Students. Computer Engineering Department, Sharif University of Technology.

    Jupyter Notebook 5

  2. IBO IBO Public

    IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology

    2

  3. Breast-Cancer-Histopathological-Image-Classification Breast-Cancer-Histopathological-Image-Classification Public

    Developed and optimized deep learning models, primarily CNNs, to classify breast cancer histopathology images into different categories. Achieved improved accuracy by leveraging advanced preprocess…

    Jupyter Notebook 1

  4. Cancer-Detection-using-Attention-Based-Network Cancer-Detection-using-Attention-Based-Network Public

    Implemented attention-based deep neural networks for accurate detection of cancer in histopathological images, enhancing model interpretability by focusing on critical regions within tissue samples.

    Jupyter Notebook 1

  5. Explainable-AI-for-Cancer-Detection-in-Histopathology Explainable-AI-for-Cancer-Detection-in-Histopathology Public

    Applied Class Activation Mapping (CAM) techniques to deep learning models for interpreting cancer detection in histopathological images, enabling visualization of discriminative regions to improve …

    Jupyter Notebook 1

  6. Explainable-Brain-Tumor-Classification Explainable-Brain-Tumor-Classification Public

    Applied Class Activation Mapping (CAM) Based techniques to deep learning models for interpreting tumor detection in MRI images, enabling visualization of discriminative regions to improve model exp…

    Jupyter Notebook