Using deep learning to enhance cancer diagnosis and classication

By Rasool Fakoor, Faisal Ladhak, Azade Nazi, Manfred Huber. 2013

258 citations recorded by the source corpus.

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In Medical Imaging

Citations in the corpus

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning (2017, background): “Deep learning has also been used as a component in more complex systems that are able to play games [32, 40, 55, 58] or diagnose and classify diseases [15, 17, 25].”
Deep Learning for Health Informatics (2017, background): “[2] proposed an autoencoder architecture based on gene expression data from different types of cancer and the same microarray dataset to detect and classify cancer.”
Deep learning for computational biology (2016, methodology): “For example, sparse autoencoders have been applied to classify cancer cases using gene expression profiles (Fakoor et al , 2013) or to predict protein backbones (Lyons et al , 2014).”
Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware (2019, methodology): “An autoencoder has been connected with principal component analysis (PCA) to learn the high-level features of 13 microarray data [25].”
Deep learning for healthcare: review, opportunities and challenges (2018, background): “(2013) Classification of cancer from gene expression profiles Stacked Sparse AE [73]”
Oblivious Neural Network Predictions via MiniONN Transformations (2017, unknown): “Machine learning is now used extensively in many application domains such as pattern recognition [10], medical diagnosis [25] and credit-risk assessment [3].”
PassGAN: A Deep Learning Approach for Password Guessing (2017, background): “Recently, there have also been significant improvements in using deep learning in health related problems [13], [19].”
The present and future of deep learning in radiology. (2019, methodology): “DL has been used in disease classification [97,98], brain cancer classification [99–101], organ segmentation [102–108], haemorrhage detection [109,110], tumor detection [111–116] are some key progresses.”
A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data (2015, methodology): “In a paper that compares stacked autoencoders with PCA with Gaussian SVM on 13 gene expression datasets, it was shown that autoencoders perform better on the majority of datasets [82].”
A Survey on Deep Learning (2018, background): “However, there is a lack of attention to this problem, which is largely attributed to the different ways in which the statisticians and machine-learning professionals use deep learning [37].”
Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal (2018, methodology): “In particular, convolutional neural networks (CNN) are widely used for imaging tasks such as classification and segmentation [22]–[25].”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “2D) for classification using SVM with Gaussian kernel [76].”
Privacy-preserving deep learning (2015, background): “Deep learning has shown promise for analyzing complex biomedical data related to cancer [13, 22, 32] and genetics [15, 56].”
Lung Nodule Classification Using Deep Features in CT Images (2015, methodology): “The methods outperforms the competing methods and achieve a maximal diagnostic accuracy of 95.52% (AD vs. NC); Wu et al. [18] use deep feature learning for deformable registration of brain MR images demonstrating that a general approach can be built to imporve image registration by using deep…”