Deep learning for magnification independent breast cancer histopathology image classification

By Neslihan Bayramoglu, Juho Kannala, Janne Heikkilä. International Conference on Pattern Recognition, 2016

385 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f7cbb1b2a56d4c74ce4920e353619103098a886e

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Transfer learning based histopathologic image classification for breast cancer detection (2018, methodology): “In this study, we are aimed to classify the histopathological breast cancer images taken from the BreaKHis database [11].”
Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization (2020, result): “As with many studies [18, 28–31], the classification accuracy of our previous CAD scheme based on the conventional method might be improved by using the DCNN.”
Deep features for breast cancer histopathological image classification (2017, methodology): “More precisely, our goal is to make use of a pre-trained CNN to extract DeCAF features, from different layers of the network, to understand whether these features are good enough to compete with visual feature descriptors, such as those presented in [3], and how they compare with deep learning based”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, neutral): “[53] CNN Histopathology images Breast cancer histopathology classification; average recognition rate was 82.”
Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model (2017, background): “Finally, despite such effective performance in the medical imaging analysis domain by deep learning 7 , existing related methods only studied on binary classification for breast cancer 8, 12, 13, 17, 18 ; however, multi-classification has more clinical values.”
Breast cancer histopathological image classification using a hybrid deep neural network. (2020, methodology): “Bayramoglu et al. [18] also used the magnification-independent deep learning method on the BreaKHis dataset, with a classification accuracy of approximately 83%.”
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network (2018, methodology): “They performed patient level classification of breast cancer with CNN and multi-task CNN (MTCNN) models and reported an 83.25% recognition rate [14].”