کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6950635 | 1451634 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer
ترجمه فارسی عنوان
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کلمات کلیدی
سرطان تخمدان، بیومارکرها، یادگیری عمیق، شبکه عصبی مکرر، زنجیره مارکوف، مونت کارلو، نمونه برداری گیبس، تشخیص تغییر نقطه، برآورد بیزی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
We present a quantitative study of the performance of two automatic methods for the early detection of ovarian cancer that can exploit longitudinal measurements of multiple biomarkers. The study is carried out for a subset of the data collected in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). We use statistical analysis techniques, such as the area under the Receiver Operating Characteristic (ROC) curve, for evaluating the performance of two techniques that aim at the classification of subjects as either healthy or suffering from the disease using time-series of multiple biomarkers as inputs. The first method relies on a Bayesian hierarchical model that establishes connections within a set of clinically interpretable parameters. The second technique is a purely discriminative method that employs a recurrent neural network (RNN) for the binary classification of the inputs. For the available dataset, the performance of the two detection schemes is similar (the area under ROC curve is 0.98 for the combination of three biomarkers) and the Bayesian approach has the advantage that its outputs (parameters estimates and their uncertainty) can be further analysed by a clinical expert.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 46, September 2018, Pages 86-93
Journal: Biomedical Signal Processing and Control - Volume 46, September 2018, Pages 86-93
نویسندگان
Manuel A. Vázquez, Inés P. Mariño, Oleg Blyuss, Andy Ryan, Aleksandra Gentry-Maharaj, Jatinderpal Kalsi, Ranjit Manchanda, Ian Jacobs, Usha Menon, Alexey Zaikin,