Mammographic breast density classification using a deep neural network: assessment on the basis of inter-observer variability

2019 ◽  
Author(s):  
Nico Kaiser ◽  
Andreas Fieselmann ◽  
Ludwig Ritschl ◽  
Steffen Kappler ◽  
Nishant Ravikumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Breast Density ◽  
Deep Neural Network ◽  
Mammographic Breast Density ◽  
Observer Variability ◽  
Inter Observer Variability

Persistent inter-observer variability of breast density assessment using BI-RADS® 5th edition guidelines

2021 ◽  
Author(s):  
Leah H. Portnow ◽  
Dianne Georgian-Smith ◽  
Irfanullah Haider ◽  
Mirelys Barrios ◽  
Camden P. Bay ◽  
...  
Keyword(s):  
Breast Density ◽  
Observer Variability ◽  
Inter Observer Variability ◽  
Density Assessment

The inter-observer variability of breast density scoring between mammography technologists and breast radiologists and its effect on the rate of adjuvant ultrasound

2016 ◽  
Vol 85 (5) ◽  
pp. 957-962 ◽  
Author(s):  
Roei D. Mazor ◽  
Avital Savir ◽  
David Gheorghiu ◽  
Yuliana Weinstein ◽  
Ifat Abadi-Korek ◽  
...  
Keyword(s):  
Breast Density ◽  
Observer Variability ◽  
Inter Observer Variability

scholarly journals Determination of mammographic breast density using a deep convolutional neural network

2018 ◽  
pp. 20180691 ◽  
Author(s):  
Alexander Ciritsis ◽  
Cristina Rossi ◽  
Ilaria Vittoria De Martini ◽  
Matthias Eberhard ◽  
Magda Marcon ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Breast Density ◽  
Deep Convolutional Neural Network ◽  
Mammographic Breast Density

scholarly journals Classification of Breast Lesions using Modified Masood Score and Neural Network

2018 ◽  
Vol 11 (3) ◽  
pp. 1745-1748
Author(s):  
Sonali Nandish Manoli ◽  
Anand Raj Ulle ◽  
N.M. Nandini ◽  
T.S. Rekha
Keyword(s):  
Neural Network ◽  
Breast Lesion ◽  
Breast Lesions ◽  
Observer Variability ◽  
Feed Forward Neural Network ◽  
Inter Observer Variability ◽  
Class 1 ◽  
Novel Method ◽  
Nuclear Features

In this paper, we propose a novel method to classify Breast Lesions based on minute changes in the cell and nuclear features of the cell. It is important to note these changes as they play a significant role in diagnosis and the line of treatment by an oncologist. To overcome the problem of inter-observer variability the method of scoring is used to grade the lesions considered for the study. We have used the Modified Masood Score and designed an algorithm which classifies a given breast lesion into 6 classes namely Benign, Intermediate class-1,Intermediate class-2, Malignant class-1,Malignant class-2 and Malignant class-3. We have developed a sensitive model using the feed-forward neural network and Pattern Network to achieve the above objective. The Rank of the features is observed using ReliefF Algorithm.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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