BIOMEDICAL IMAGE ANALYSIS

BMI/COMPSCI 771

Addresses the problems of representation and reasoning for large amounts of visual data, including images and videos, medical imaging data, and their associated tags or text descriptions. Introduces deep learning in the context of computer vision. Covers topics on visual recognition using deep models, such as image classification, object detection, human pose estimation, action recognition, 3D understanding, and medical image analysis. Emphasizes the design of vision and learning algorithms and models, as well as their practical implementations. Strongly recommended to have knowledge in computer vision or machine learning [such asCOMP SCI 540] or medical image analysis [such as B M I /COMP SCI/​B M I  567].

BMI/STAT 768

Introduce key statistical methods and concepts for analyzing various medical images. Analyze publicly available and student/instructor supplied imaging data using the most up-to-date methods and tools.

COMPSCI/ECE 766

Fundamentals of image analysis and computer vision; image acquisition and geometry; image enhancement; recovery of physical scene characteristics; shape-from techniques; segmentation and perceptual organization; representation and description of two-dimensional objects; shape analysis; texture analysis; goal-directed and model-based systems; parallel algorithms and special-purpose architectures. Students are strongly encouraged to have basic proficiency in calculus and linear algebra, such asMATH 340, and basic programming such asCOMP SCI 300.

BMI 826

Covers advanced topics in the areas of biostatistics and biomedical informatics. Includes reading and discussion of original literature and individual student projects.

BMI/COMPSCI 767

Review of advanced medical image analysis techniques. Covers advanced segmentation and registration methods. Describes the use and extension of statistical and machine learning methods for medical image analysis tasks.