Deep learning has emerged as a powerful approach to address complex problems in various fields, including biology. In this four-part series of vLEs, we will describe the theory and application of two deep learning models - the multiplayer perceptron and the convolutional neural network.
Deep learning algorithms offer a powerful means to automatically analyze the content of biomedical images. However, many biological samples of interest are difficult to resolve with a standard optical microscope.
Natural language processing (NLP) is a field focused on developing automated methods for analyzing text, and also for computer-driven text generation (synthesis, for example in translation and text summarization).
Medical image analysis with machine learning holds immense promise for accelerating the radiology workflow and benefiting patient care. Computed tomography (CT) is a medical imaging technique that produces a high-resolution volumetric image of the internal organs.
COVID-19 has led to the rapid adoption of telehealth strategies in order to maintain continuity of care. As compared to in-person visits, important changes in patient characteristics were seen in telephone and video visits as well as clinician ordering patterns.
Data visualization is part art and part science. A data visualization has to accurately convey the data, but also should be aesthetically pleasing. Great visual presentations of data will enhance the message and lead to deeper understanding of the underlying data.
The goal of computer vision is for computers to be able to understand visual content (e.g. images, videos, 3D, stereo), usually for the purpose of making predictions (classification, detection, captioning, generation, etc.).
The convolutional neural network (CNN) represents the current state-of-the-art for image and video analysis, and is increasingly used for analyzing time series and other data with spatial or sequential structure.
Deep natural language (NLP) processing models have achieved great success to improve language understanding for real-world applications, i.e., question and answering, translation, etc. The Transformer is the most powerful approach among those tools.
Neural-network-based methods for natural language processing (NLP) constitute an area of significant recent technical progress, with many interesting real-world applications. The Transformer Network is one of the newest and most powerful approaches of this type.