Computer Science & Engineering Department

CS791 Topics: Mass Detection in Mammograms (Fall 2023)

  • Meets: MW 2:30pm - 3:45pm (WPEB 200)

  • Instructors: Dr. George Bebis and Dr. Alireza Tavakkoli
  • Assistants: Prithul Sarker

  • Prerequisites

    Background in the following areas would be very useful: image processing, computer vision, pattern recognition, machine learning, and deep learning. Knowledge or desire to quickly learn Jupyter Notebook and Python is required. Familiarity with Keras/Tensorflow or Pytorch would be a plus.


    We will not use any text in this course; most of the material will be drawn from research papers.


    Dataset-related Papers

    Useful Videos

    Interesting Stories (quick reads)

    AI in Breast (and Medical) Imaging

    Radiomics (i.e., extraction of quantitative features from medical images)

    Useful Resources

    Description and Objectives

    The course will focus on the problem of mass detection and classification in mammograms and possibly other modalities such as CT and MRI. The goal is to expose students to some of the main challenges involved in this research area and to recent methods developed by the research community to address these challenges. The course is primarily intended for highly motivated students who are interested in applying pattern recognition, machine learning and deep learning techniques to this research area.


    There will be no exams in this course. Grading will be based on paper presentations, class participation, and a team project.


    Schedule (tentative)

    Papers for Presentation

    Presentation Tips

    Department of Computer Science & Engineering, University of Nevada, Reno, NV 89557
    Page created and maintained by: Dr. George Bebis (