Deep Learning
Deep learning-based classification is the most powerful and widely used classification method in the Visiopharm toolbox. It often significantly outperforms other classification methods for both standard and complex analysis tasks.
At the same time, deep learning is among the more advanced classification methods available. Compared to other approaches, it introduces additional concepts, workflows, and configuration options. This section aims to demystify deep learning by introducing the fundamental concepts behind deep neural networks, explaining how deep learning models are used within Visiopharm, and documenting the available advanced configuration options.
The Deep Learning documentation is divided into three main sections:
This section provides a general introduction to deep learning concepts. It explains, at a conceptual level, how deep learning models are built, how they learn from data, and how they generate predictions. It also introduces the different neural network architectures supported in Visiopharm and explains how they differ from each other.
Using Deep Learning in Visiopharm
This section focuses on how to use deep learning for classification in Visiopharm. It provides a step-by-step guide to creating, training, and applying a deep learning app in Visiopharm. It also explains how to understand and configure the basic training parameters, how to handle common errors, as well as how to use TensorBoard to monitor and better understand the training process.
AI Architect - Advanced Deep Learning Settings
This section explains how to configure and fine-tune deep learning models using the AI Architect in order to optimize performance for specific tasks and datasets.
Deep learning and convolutional neural networks are a broader family of AI machine learning methods. It involves neural network algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation with each successive layer using the output from the previous layer as input. Using deep learning for classification allows you to segment abstract image structures that would be impossible to segment with a simple pixel classifier.
Network Architecture U-Net (Default), DeepLabv3+ (Requires GPU) and FCN-8s are three different network architectures. We generally recommend choosing U-Net over FCN-8s and DeepLabv3+, which is emphasized by the (Default) tag.
Convergence Curve is a simple training plot showing the learning performance of the neural network. The x-axis shows the number of iterations, while the y-axis shows the training loss or error rate depending on the settings. The settings are found in the dropdown menu of the training plot icon 
.
Loss is a measure used determine the performance of the network optimization algorithm to a scalar value by measuring the inconsistency between predicted and actual values. The robustness of the model increases with the decrease of training loss. When the training is paused or stopped, the final loss value is shown. The training loss is available from the dropdown menu next to the training plot icon . When selected, the loss of the network is visualized in the training plot and next to the network architecture.
Metric is a measure used to evaluate the network algorithm. The performance metric used is the error rate. The error rate percentage fluctuates and decreases during training until the model has converged. When the training is paused or stopped, the final error rate percentage is shown. The training metric is available from the dropdown menu next to the training plot icon . When selected, the error rate of the network is visualized in the training plot and next to the network architecture.
Iterations are the number of times a batch of data has passed through the network algorithm. The number of iterations are visualized in the training plot and next to the training loss or error rate.
Probability value is determined by the slider position indicates the minimum probability needed in order to classify a pixel as one of the image classes. The deep learning classifier will classify a pixel to the class having the highest probability, given that it is above this minimum value. As an example, consider a pixel having a probability of 40% for belonging to class A, 35% for class B and 25% for class C. If the minimum probability value is set to 50%, the pixel will not be classified as any of the classes. On the contrary, if a value below 40% is chosen, the pixel will be classified as class A. The default probability is 0% meaning that all pixels will be classified and given a label.
Advanced Deep learning settings By pressing the menu button 
of the APP author, the AI Architect dialog appears for advanced deep learning settings.