Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data

By Hoo-Chang Shin, Matthew R. Orton, David J. Collins, Simon J. Doran, Martin O. Leach. 2013

498 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Representation & Self-Supervised Learning, Medical Imaging

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Citations in the corpus

Distributed learning of deep neural network over multiple agents (2018, background): “In situations with fewer labeled data-samples, a reasonable approach includes learning hierarchical representations using unsupervised learning [31].”
Relational autoencoder for feature extraction (2017, background): “Subsequent studies show that stacked au-toencoder model can learn meaningful, abstract features and thus achieve better classiﬁcation results in high-dimensional data, such as images and texts [38–41].”
Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning (2016, background): “, N , the weight matrices W1 and W2 and the bias vectors b1 and b2 are learned by minimizing the following cost function [42]:”
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis (2013, methodology): “Furthermore, inspired from the recent computer vision researches (Ngiam et al. 2011; Srivastava and Salakhutdinov 2012), we can efﬁ-ciently handle the incomplete data problem (Yuan et al. 2012) with multi-modal deep learning.”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, unknown): “The work in Shin, Orton, Collins, Doran, and Leach (2012) trained Dynamic Contrast-Enhanced MRI (DCE-MRI) 78 images of patients ’ livers and kidneys using a DL approach.”
Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans (2016, methodology): “…to the medical image analysis domain with promising results on various applications, like the computerized prognosis for Alzheimer’s disease and mild cognitive impairment 32 , organ segmentations 33 and detection 34 , ultrasound standard plane selection 35 , etc., on 3D or 4D image data, etc.”
Stacked Convolutional Denoising Auto-Encoders for Feature Representation (2017, methodology): “[21] applied stacked sparse auto-encoders (SSAEs)”
Deep Learning and Its Applications in Biomedicine (2018, methodology): “SAE Segmentation of hippocampus from infant brains [116] Organ detection in 4D patient data [122] Histological characterization healthy skin and healing wounds [124] Scoring of percentage mammographic density and mammogra ic texture related to breast cancer risk [134] Optic disc detection from fundu”
Two-Stage Learning to Predict Human Eye Fixations via SDAEs (2016, methodology): “For the other hyperparameters, we use a coordinate ascent-like method [56]”
Deep learning in medical imaging and radiation therapy. (2018, methodology): “Unsupervised learning in DL is generally performed by autoencoders or independent subspace analysis (ISA).(249,250,311) The outputs of these networks may be further processed in a supervised manner, by extracting the features from the network and applying a machine learning classifier.”
Automated detection and classification of the proximal humerus fracture by using deep learning algorithm (2018, background): “Deep learning comprises a neural network with multiple hidden layers that enhance image recognition accuracy, thereby increasing its versatility for capturing representative features (Shin et al. 2013).”
Deep Learning Applications in Medical Image Analysis (2018, methodology): “[63] used stacked autoencoders on 78 contrast-enhanced MRI scans of the abdominal region containing liver or kidney metastatic tumors, to detect the locations of the liver, heart, kidney and spleen.”
Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys (2017, background): “…approach that performs a series of downsampling (i.e., max pooling operations which select the maximum value from a patch of features which help to reduce the data dimensionality) and upsampling procedures (similar to autoencoders [26], which allow classification to be made at the voxel level).”