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Point-of-care testing (POCT), also called near-patient testing or bedside testing, is defined as medical diagnostic testing at or near the point of care—that is, at ...
A fully connected layer for an image of size 100 × 100 has 10,000 weights for each neuron in the second layer. Convolution reduces the number of free parameters, allowing the network to be deeper. [6] For example, using a 5 × 5 tiling region, each with the same shared weights, requires only 25 neurons.
An analyte is a substance, chemical or biological, that is being analyzed using a certain instrument. While point-of-care testing is the quantification of one analyte from one in vitro (e.g., blood, plasma or urine) sample, multiplexed point-of-care testing is the simultaneous on-site quantification of various analytes from a single sample. [2]
Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly passing through multiple intermediate layers (hidden layers). A network is typically called a deep neural network if it has at ...
A bottleneck block [1] consists of three sequential convolutional layers and a residual connection. The first layer in this block is a 1x1 convolution for dimension reduction (e.g., to 1/2 of the input dimension); the second layer performs a 3x3 convolution; the last layer is another 1x1 convolution for dimension restoration.
The first version of Flutter was known as "Sky" and ran on the Android operating system. [31] It was unveiled at the 2015 Dart developer summit with the stated intent of being able to render consistently at 120 frames per second. [31] On December 4, 2018, Flutter 1.0 was released at the Flutter conference in London. [32]
Point of care (POC) documentation is the ability for clinicians to document clinical information while interacting with and delivering care to patients. [10] The increased adoption of electronic health records (EHR) in healthcare institutions and practices creates the need for electronic POC documentation through the use of various medical devices. [11]
LeNet-4 was a larger version of LeNet-1 designed to fit the larger MNIST database. It had more feature maps in its convolutional layers, and had an additional layer of hidden units, fully connected to both the last convolutional layer and to the output units. It has 2 convolutions, 2 average poolings, and 2 fully connected layers.