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Stanford Courses

Biomedin 260 Computational Methods for Biomedical Image Analysis and Interpretation

Quarter: Each Spring

Course Description:

Beginning with the latest biological and medical imaging modalities and their applications in research and medicine, this class focuses 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. With the permission of the instructor, students may enroll for 3 units and participate with reduced project requirements.

CS 523 Research Seminar in Computer Vision and Healthcare

Quarter: Spring 2021

Course Description

With advances in deep learning, computer vision (CV) has been transforming healthcare, from diagnosis to prognosis, from treatment to prevention. Its far-reaching applications include surgical assistants, patient monitoring, data synthesis, and cancer screening. Before these algorithms make their way into the clinic, however, there is exciting research to develop methods that are accurate, robust, interpretable, grounded, and human-centered. In this seminar, we deeply examine these themes in medical CV research through weekly intimate discussions with researchers from academia and industry labs who conduct research at the center of CV and healthcare. Each week students will read and prepare questions and reflections on an assigned paper authored by that week's speaker. We highly encourage students who are interested in taking an interactive, deep dive into medical CV research literature to apply. While there are no hard requirements, we strongly suggest having the background and fluency necessary to read and analyze AI research papers (thus MATH 51 or linear algebra, and at least one of CS 231x, 224x, 221, 229, 230, 234, 238, AI research experience for CV and AI fundamentals may be helpful). Interest form/application can be filled out here.

CS 106A Code in Place

Quarter: Spring 2021

Course Description

CS106A is one of most popular courses at Stanford University, taken by almost 1,600 students every year. It has been developed over the last 30 years by an amazing team, including Nick Parlante, Eric Roberts and more. The course teaches the fundamentals of computer programming using the widely-used Python programming language. This course is for everyone from humanists, social scientists, to hardcore engineers. Code in Place is built off the first half of CS106A.

Code in Place require no previous background in programming — just a willingness to work hard and a love for learning. It requires considerable dedication and hard work, over a course of 5 weeks.

MED 18SI Artificial Intelligence in Medicine and Healthcare Ventures

Quarter: Spring 2021

Course Description

The face of healthcare is changing - innovative technologies, based on recent advances in artificial intelligence, are radically altering how care is delivered. Startups are offering entirely new ways to diagnose, manage, treat, and operate. Few ever reach the patient - those that do have much more than an idea and an algorithm; they have an intimate understanding of the healthcare landscape and the technical knowhow to successfully integrate AI solutions into the medical system. In this course, we tackle the central question: How can young students find feasible and impactful medical problems, and build, scale, and translate technology solutions into the clinic. Together, we will discover the transformative technologies of tomorrow that we can build today. Please see the syllabus for more information. We encourage students of all backgrounds to enroll- the only prerequisite is a strong passion for technology in healthcare.

BIODS 388 / BMI 388 / MED 288 Stakeholder Competencies for Artificial Intelligence in Healthcare

Quarter: Fall 2020

Course Description

Advancements of machine learning and AI into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. But we will never realize the potential for these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles - this will allow successful, responsible development and deployment of these systems into the healthcare domain. The focus of this course is on the key concepts and principles rather than programming or engineering implementation. Those with backgrounds in healthcare, health policy, healthcare system leadership, pharmaceutical, and clinicians as well as those with data science backgrounds who are new to healthcare applications will be empowered with the knowledge to responsibly and ethically evaluate, critically review, and even use these technologies in healthcare. We will cover machine learning approaches, medical use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing, building, and evaluating machine learning in healthcare applications.

CS 522 Seminar in Artificial Intelligence in Healthcare

Quarter: Fall 2020

Course Description:

Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, genomics, surgical robotics, and drug discovery. In the coming years, artificial intelligence has the potential to lower healthcare costs, identify more effective treatments, and facilitate prevention and early detection of diseases. This class is a seminar series featuring prominent researchers, physicians, entrepreneurs, and venture capitalists, all sharing their thoughts on the future of healthcare. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary). Speakers and more at CS522 Seminar in AI in Healthcare.