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Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications. |
| 개인저자 | Sun, Ruoxi. |
| 단체저자명 | Columbia University. Biological Sciences. |
| 발행사항 | [S.l.]: Columbia University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 113 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781392688472 |
| 학위논문주기 | Thesis (Ph.D.)--Columbia University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Includes supplementary digital materials. Advisor: Paninski, Liam. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Developments in modern bio-imaging techniques have allowed the routine collection of a vast amount of data from various techniques. The challenges lie in how to build accurate and efficient models to draw conclusions from the data and facilitate scientific discoveries. Fortunately, recent advances in statistics, machine learning, and deep learning provide valuable tools. This thesis describes some of our efforts to build scalable Bayesian models for four bio-imaging applications: (1) Stochastic Optical Reconstruction Microscopy (STORM) Imaging, (2) particle tracking, (3) voltage smoothing, (4) detect color-labeled neurons in c elegans and assign identity to the detections. |
| 일반주제명 | Biostatistics. Statistics. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
