Stanford Root

Schedule

Stanford Root

Schedule

CS 235

Computational Methods for Biomedical Image Analysis and Interpretation (BMDS 260, BMP 260, RAD 260)

UNITS:3
GRADING:Medical Option (Med-Ltr-CR/NC)
LEVEL:Graduate
GER:—

The latest biological and medical imaging modalities and their applications in research and medicine. Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science. Includes a project. Enrollment for 3 units requires instructor consent. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. Knowledge of Matlab or Python highly recommended.

Syllabus not available for this section

Sections

0 Terms
No sections available.

CS 235: Computational Methods for Biomedical Image Analysis and Interpretation (BMDS 260, BMP 260, RAD 260)

3 units · Medical Option (Med-Ltr-CR/NC)

The latest biological and medical imaging modalities and their applications in research and medicine. Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science. Includes a project. Enrollment for 3 units requires instructor consent. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. Knowledge of Matlab or Python highly recommended.

More CS courses

  • CS 229: Machine Learning (STATS 229)
  • CS 230: Deep Learning
  • CS 231A: Computer Vision: From 3D Perception to 3D Reconstruction and Beyond
  • CS 231N: Deep Learning for Computer Vision
  • CS 233: Geometric and Topological Data Analysis (CME 251)
  • CS 234: Reinforcement Learning
  • CS 236G: Generative Adversarial Networks
  • CS 237A: Principles of Robot Autonomy I (AA 274A, EE 260A, ME 274A)
  • CS 237B: Principles of Robot Autonomy II (AA 174B, AA 274B, EE 260B, ME 274B)
  • CS 238: Decision Making under Uncertainty (AA 228)
  • CS 238V: Validation of Safety Critical Systems (AA 228V)
  • CS 239: Advanced Topics in Sequential Decision Making (AA 229)

All CS courses · All departments