(http://onclass.ds.czbiohub.org/) and as part of the package we provided a pre-trained model that can output cell type annotations for millions of cells in a few minutes on a modern server. By leveraging the structure of the cell ontology, OnClass pushes the boundaries of automated ...
Given the complex structure of the dataset and the existence of many tunable hyperparameters in each ML model, it was crucial to perform a crude optimization of those hyperparameters. For each model, we have selected the most important tunable hyperparameters and performed a grid search of those...
we aimed to identify simple models that could both effectively reduce the biological space to a set of useful parameters for cell type classification and recreate spiking behavior for a diverse set of neurons for use in network models. In the adult cortex, the majority of communication...
This is a multi-layer neural network composed of neurons with trainable weights and biases [9] and it is made possible by powerful GPUs that enable us to stack deep layers and handle a wide range of image input properties [10]. LG has been widely used as a complete data processing ...
(RGB). Some alterations that made this model successful were the use of a dice-loss coefficient, Adam optimizer and a dropout function after each convolutional layer which provided losses, accuracies and dice scores of up to 0.03, 0.98 and 0.97, respectively. The model was tested with five-...
It has three additional FC layers at the end of the VGG16 model consisting of 4096, 4096, and 1000 neurons, respectively, making it a total of 19 layers. Additionally, the convolutional layers are arranged with the rectified linear unit (ReLU) activation function [25]. Training Approach To ...
For the MLP algorithm, MLPClassifier with 2 hidden layers, 100 neurons in the hidden layer, using Adam optimization algorithm [43] and ReLU activation function [44] was used. The learning rate was 0.001, the momentum coefficient was 0.8, and the epoch number was 200. ...
where many neurons in the network end up outputting a constant zero value and therefore stop learning [31]. Moreover, the Leaky ReLU activation function offers a notable advantage over ReLU by providing a non-zero for negative input values. This characteristic enables Leaky ReLU to mitigate the...
The grey image value in the [0, 255] range is rescaled to [0, 1] using the Keras preprocessing rescaling function, and this value is passed as the input of the first convolutional layer. Convolutional layers Conv1, Conv2 have 32 neurons, and Conv3 has 64 neurons, each with a 3 × ...