Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network

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NAS-Unet: Neural Architecture Search for Medical Image Segmentation (2019, background): “recognition of images at the pixel level (the object category to each pixel in the image belongs is marked) [4], [5].”
Deep learning in medical image registration: a review (2019, methodology): “Recently, convolutional autoencoder (CAE) was proposed to combine CNN with traditional autoencoders [15].”
Deep learning in medical image registration: a survey (2019, methodology): “d autoencoders include sparse autoencoder, denoising autoencoder and contractive autoencoder [151]. Recently, convolutional autoencoder (CAE) was proposed to combine CNN with traditional autoencoders [15]. CAE replaces the fully connected layer in traditional AE with convolutional layers and transpo”
Recent advances and clinical applications of deep learning in medical image analysis (2021, methodology): “To efficiently learn nodule features, the 3D faster RCNN had the U-Net-like structure (Ronneberger et al., 2015) and was built with compact dual path blocks (Chen et al., 2017b).”
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring (2020, methodology): “Generative Convolution Autoencoder (GCAE) takes advantage of the feature extraction ability of CNN, di-mension reduction capability of autoencoder and over-sampling strategy of SMOTE [40].”