CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients

27 May 2020 • Dani Kiyasseh • Tingting Zhu • David A. Clifton

The healthcare industry generates troves of unlabelled physiological data. This data can be exploited via contrastive learning, a self-supervised pre-training method that encourages representations of instances to be similar to one another... (read more)

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METHOD	TYPE
Batch Normalization	Normalization
1x1 Convolution	Convolutions
Residual Connection	Skip Connections
Bottleneck Residual Block	Skip Connection Blocks
Max Pooling	Pooling Operations
Residual Block	Skip Connection Blocks
Dense Connections	Feedforward Networks
Kaiming Initialization	Initialization
Average Pooling	Pooling Operations
Convolution	Convolutions
Global Average Pooling	Pooling Operations
Random Resized Crop	Image Data Augmentation
ResNet	Convolutional Neural Networks
ReLU	Activation Functions
Feedforward Network	Feedforward Networks
Random Gaussian Blur	Image Data Augmentation
ColorJitter	Image Data Augmentation
NT-Xent	Loss Functions
SimCLR	Self-Supervised Learning

METHOD

TYPE

Batch Normalization