-
Patent Agency Ranking
公开(公告)号：WO2017040669A1
公开(公告)日：2017-03-09
申请号：PCT/US2016/049706
申请日：2016-08-31
Applicant: PRESIDENT AND FELLOWS OF HARVARD COLLEGE
Inventor： KLECKNER, Nancy E. , CHANG, Frederick S.
IPC: G02B27/46
CPC classification number: G02B27/58 , G01N21/6458 , G02B21/16 , G06K9/00127 , G06K9/6277 , G06T5/002
Abstract: Methods and systems for detecting and characterizing a pattern (or patterns) of interest in a low signal-to-noise ratio (SNR) data set are disclosed. One method is a two-stage Likelihood pipeline analysis that takes advantage of the benefits of a full Likelihood analysis while providing computational tractability. The two-stage pipeline may include a first stage including the application of approximate Likelihood functions in which one or more of the following assumptions or modifications may be applied: (i) the pattern of interest and background are at a specified position in a segment of the data set under examination; (ii) the SNR is low; and (iii) measurement noise can be represented in such a form that all non-position parameters of the representation are linear with respect to the derivative of the Log Likelihood versus lambda. The second stage may include a full Likelihood analysis.
Abstract translation: 公开了用于检测和表征低信噪比（SNR）数据集中的感兴趣的模式（或模式）的方法和系统。 一种方法是两阶段似然流水线分析，其利用完全似然分析的优点，同时提供计算易处理性。 两级流水线可以包括第一级，其包括应用近似似然函数，其中可以应用以下假设或修改中的一种或多种：（i）感兴趣和背景的模式位于 正在审查的数据集 （ii）SNR较低; 和（iii）测量噪声可以以这样的形式表示，即所述表示的所有非位置参数相对于对数似然的导数与λ线性相关。 第二阶段可能包括完整的似然分析。
-
公开(公告)号：WO2019140434A2
公开(公告)日：2019-07-18
申请号：PCT/US2019/013614
申请日：2019-01-15
Applicant: PRESIDENT AND FELLOWS OF HARVARD COLLEGE
Inventor： KLECKNER, Nancy E. , CHANG, Frederick S.
IPC: G06T7/13
Abstract: Methods and systems for detecting and characterizing a pattern (or patterns) of interest in a low signal-to-noise ratio (SNR) data set are disclosed. One method is a form of a two-stage Likelihood pipeline analysis for differentiating multiple closely-spaced spots that takes advantage of the benefits of a full Likelihood analysis while providing computational tractability. The two-stage pipeline may include a first stage including the application of approximate Likelihood functions. The second stage may include a full Likelihood analysis. Once a pattern of interest instance is characterized, it may be subtracted from the underlying data, and the two-stage analysis may be performed on the reduced data to detect a further pattern of interest proximate the characterized pattern.
-
公开(公告)号：WO2019140430A1
公开(公告)日：2019-07-18
申请号：PCT/US2019/013605
申请日：2019-01-15
Applicant: PRESIDENT AND FELLOWS OF HARVARD COLLEGE
Inventor： KLECKNER, Nancy E. , CHANG, Frederick S.
Abstract: Methods and systems for detecting and characterizing a pattern (or patterns) of interest in a low signal-to-noise ratio (SNR) data set are disclosed. One method is a two-stage Likelihood pipeline analysis for detecting patterns of interest captured with multiple data capture regimes that takes advantage of the benefits of a full Likelihood analysis while providing computational tractability. The two-stage pipeline may include a first stage including the application of approximate Likelihood functions. At the first stage, the effects of penetrance of each pattern of interest into each data capture regime may be accounted for. The second stage may include a full Likelihood analysis.
-
公开(公告)号：WO2019140434A3
公开(公告)日：2019-07-18
申请号：PCT/US2019/013614
申请日：2019-01-15
Applicant: PRESIDENT AND FELLOWS OF HARVARD COLLEGE
Inventor： KLECKNER, Nancy E. , CHANG, Frederick S.
Abstract: Methods and systems for detecting and characterizing a pattern (or patterns) of interest in a low signal-to-noise ratio (SNR) data set are disclosed. One method is a form of a two-stage Likelihood pipeline analysis for differentiating multiple closely-spaced spots that takes advantage of the benefits of a full Likelihood analysis while providing computational tractability. The two-stage pipeline may include a first stage including the application of approximate Likelihood functions. The second stage may include a full Likelihood analysis. Once a pattern of interest instance is characterized, it may be subtracted from the underlying data, and the two-stage analysis may be performed on the reduced data to detect a further pattern of interest proximate the characterized pattern.
-
公开(公告)号：WO2019140428A1
公开(公告)日：2019-07-18
申请号：PCT/US2019/013598
申请日：2019-01-15
Applicant: PRESIDENT AND FELLOWS OF HARVARD COLLEGE
Inventor： KLECKNER, Nancy E. , CHANG, Frederick S.
IPC: G02B27/46
CPC classification number: G02B27/46
Abstract: Methods and systems for detecting and characterizing a pattern (or patterns) of interest in a low signal-to-noise ratio (SNR) data set are disclosed. One method is a two-stage Likelihood pipeline analysis that takes advantage of the benefits of a full Likelihood analysis while providing computational tractability. The two-stage Likelihood pipeline may include, at either stage, the calculation and use of one or more of a false positive rate, a standard error, and a multi-factor landscape involving a gradient and/or Hessian of a Likelihood ratio.
-
-
-
-
Search Results
5
records
(took 0.011 seconds)
