Deep Learning for Breast Cancer Diagnosis from Mammograms-A Comparative Study.
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TLDR
Deep convolutional neural networks are investigated in the context of computer-aided diagnosis (CADx) of breast cancer, showing the superior performance achieved in the case of fine-tuning a pretrained network compared to training from scratch.Abstract:
Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a pretrained network compared to training from scratch.read more
Citations
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Journal ArticleDOI
Inconsistent Performance of Deep Learning Models on Mammogram Classification.
TL;DR: The results demonstrate performance inconsistency across the data sets and models, indicating that the high performance of deep learning models on one data set cannot be readily transferred to unseen external data sets, and these models need further assessment and validation before being applied in clinical practice.
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Deep learning in mammography images segmentation and classification: Automated CNN approach
Wessam M. Salama,Moustafa H. Aly +1 more
TL;DR: End-to-end fully convolutional neural networks (CNNs) are introduced in this paper and the proposed technique of applying data augmentation with modified U-Net model and InceptionV3 achieves the best result, specifically with the DDSM dataset.
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Deep feature–based automatic classification of mammograms
TL;DR: Results show that the proposed methodology is a promising and robust CADx system for breast cancer classification and the deep ensemble extracting the robust features with the final classification using neural networks.
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Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review.
TL;DR: A systematic review of the literature on artificial neural network (ANN) based models for the diagnosis of breast cancer via mammography found that the best performance was achieved by residual neural network-50 and ResNet-101 models of CNN algorithm.
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A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images
TL;DR: This research proposes a novel convolution neural network (CNN) model for the detection of architectural distortion by enhancing its performance using data augmentation technique and investigates the performance of the proposed model on different operations of image augmentation.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.