کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4944098 1437978 2018 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Sparse regression with output correlation for cardiac ejection fraction estimation
ترجمه فارسی عنوان
رگرسیون پراکنده با همبستگی خروجی برای برآورد کسر جهشی قلب
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Traditional regression methods minimize the sum of errors of samples with various regularization terms such as the ℓ1-norm and ℓ2-norm. For the diagnosis of cardiovascular diseases, the cardiac ejection fraction (EF) represents an essential measure. However, existing regularization terms do not consider the output correlation (the correlation between ground truth volumes and estimated volumes w.r.t. each subject), which is beneficial in estimating the cardiac EF. In this paper, we first propose a sparse regression with two regularization terms of the ℓ1-norm and output correlation (SROC). Then, we propose a one-dimensional solution path algorithm for quickly finding two good regulation parameters in the formulation of SROC. The solution path algorithm can effectively handle singularities and infinities in the key matrix. Finally, we conduct experiments on a clinical cardiac image dataset with 100 subjects. The experimental results show that our method produces competitive and strong results for estimating the cardiac EF based on quantitative and qualitative analyses.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 423, January 2018, Pages 303-312
نویسندگان
, , , , ,