Computer Science & Engineering Department


CS791 Topics: Mass Detection in Mammograms (Fall 2025)


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.

Textbook

No textbook will be used in this course. Most materials will be drawn from research papers.

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.

Requirements

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

Handouts


DL Books

DL Courses

DL Tutorials

DL Environments and Libraries

DL Review Slides (Dr Tavakkoli)

DL papers written with radiologists in mind


Interesting Stories on AI and Mammography (quick reads)


Schedule (tentative)


Datasets

Datasets-related Papers

Presentation Tips


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