Now showing items 1-3 of 3

    • Exploratory Assessment of K-means Clustering to Classify 18F-Flutemetamol Brain PET as Positive or Negative 

      Zukotynski, Katherine; Black, Sandra E.; Kuo, Phillip H.; Bhan, Aparna; Adamo, Sabrina; Scott, Christopher J.M.; Lam, Benjamin; Masellis, Mario; Kumar, Sanjeev; Fischer, Corinne E.; Tartaglia, Maria Carmela; Lang, Anthony E.; Tang-Wai, David F.; Freedman, Morris; Vasdev, Neil; Gaudet, Vincent C. (Wolters Kluwer Health, 2021)
      Rationale: We evaluated K-means clustering to classify amyloid brain PETs as positive or negative. Patients and Methods: Sixty-six participants (31 men, 35 women; age range, 52–81 years) were recruited through a multicenter ...
    • Machine Learning in the Nuclear Medicine: Part 1-Introduction 

      Uribe, Carlos F.; Mathotaarachchi, Sulantha; Gaudet, Vincent C.; Smith, Kenneth C.; Rosa-Neto, Pedro; Benard, Francois; Black, Sandra E.; Zukotynski, Katherine (Society of Nuclear Medicine and Molecular Imaging, 2019-04)
      This article, the first in a 2-part series, provides an introduction to machine learning (ML) in a nuclear medicine context. This part addresses the history of ML and describes common algorithms, with illustrations of when ...
    • Machine Learning in the Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects 

      Zukotynski, Katherine; Gaudet, Vincent C.; Uribe, Carlos F.; Mathotaarachchi, Sulantha; Smith, Kenneth C.; Rosa-Neto, Pedro; Benard, Francois; Black, Sandra E. (Society of Nuclear Medicine and Molecular Imaging, 2021-01)
      This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural ...

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