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
- Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
- Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- Additional books are available on Canvas
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
1. Presentations should be professional as if it was presented in a formal conference (i.e., powerpoint slides).
2. Your goal is to educate and inform your audience. Make sure your presentation follows a logical sequence. Help the audience understand how successive definitions and results are related to each other and to the big picture.
3. You should have your remarks prepared and somewhat memorized. Reading from your notes excessively should be avoided.
4. Anticipate Questions: think of some likely questions and plan out your answer. Understand the Question: paraphrase it if necessary; repeat it if needed. Do Not Digress. Be Honest: if you can't answer the question, say so.
5. Meet the eyes of your audience from time to time.
6. Vary the tone of your voice and be careful to speak clearly and not talk too quickly.
Department of Computer Science & Engineering, University of Nevada, Reno, NV 89557
Page created and maintained by:
Dr. George Bebis
(bebis@unr.edu)