Course
CS584 - Biomedical Image Analysis (Spring 2021)
With the advancement of deep learning, the field of biomedical imaging has exploded in recent years - but for the uninitiated, even loading nth dimensional data can be a challenge!
This course will provide a broad overview of biomedical image analysis as well as explore the foundational techniques required to read, process, analyze and use images for scientific discovery and applications. The course will discuss both traditional computer vision and machine learning techniques. We expect that the students will have basic understanding of machine learning.
Case studies include linking image data to phenotypic and clinical data, developing representations of image phenotypes and research applications. Students will participate in a final project.
50$ google cloud credit (GCP) availble for each student.
Prerequisite:
Linear Alzebra, machine learning, Coding experience in Python and Matlab
Syllabus:
Introduction of Biomedical image analysis
Exploration of the data space (nth dimension)
Signal processing - sampling theory and interpolation
Masks and filters - Contrast adjustment, denoising (convolution, FFT), deblurring, edge detection, anisotropic diffusion, super-resolution.
Object Detection (RCNN, Fast RCNN)
Segmentation (region growing, snake, level-sets, UNet)
Transformation and registration
Measurements (radiomics)
Classification and Prognosis
Final project
Types of activities:
Homework, Assignments, and final project.
Types of Technologies:
Canvas will be the main platform for course work update, and we will use Canvas Forums, Zoom, and others that may come up in our discussions. Coding will be perfromed in Python and Matlab.
CS 170 - Introduction to Programming (Summer 2020, Summer 2021)
This course is an introduction to computer science to make serious use of the computer in course work or research.
Topics include: fundamental computing concepts, general programming principles, the Unix Operating System, and the Java programming language. Emphasis will be on algorithm development with examples highlighting topics in data structures. You will gain the practical skillset needed to write interactive, graphical programs at an introductory level using JAVA. This course is the first of a two semester sequence for computer science majors and is followed by CS171.
Each week runs from Monday to Sunday. Each assignment/deliverable has specific due dates indicated on the Course Schedule and the Course Calendar. There may be some overlap of assignments. For example, an assignment started at the beginning of a module may be due in the middle of the next module or later. You can always turn something in early!
Textbook
Required: CS170@Emory textbook
Optional: "Introduction to Java Programming", Brief Version, Y. Daniel Liang. You can get either the 8th, 9th, or 10th edition from Amazon if you wish.
Prerequisites
There are no prerequisites although some familiarity with software installation will be helpful. We assume knowledge of high school algebra and basic problem solving skills.
Types of Activities
Over the next three weeks, we will engage in foundational activities inherent to the online classroom that will be afforded by technologies. These activities will include
Homework
Assignment
Exam
Collaboration
Discussion
Types of Technologies
Canvas will be the main platform for course work update, and we will use Canvas Forums, Zoom, and others that may come up in our discussions.
Java compilers to be installed in your computer before (let me or the TA know if you have any issue):
Choice 1: JDK https://www.oracle.com/java/technologies/javase-jdk8-downloads.html
Choice 2: Netbeans https://netbeans.org/downloads/8.2/rc/
Online (only for quick checking): https://repl.it/languages/java10