![]() Notwithstanding the terminology, an ANN does not work the same way a biological neuron works, but the fundamental idea of response to input being selectively passed forward in a network is similar. ANNs comprise an end-to-end process, where the neural network learns, extracts, and selects those features most appropriate for the given task. This provided the groundwork for the eventual use of artificial neural networks (ANNs) in machine learning. Neural networks were first proposed by Warren McCulloch and Walter Pitts in 1943. For completeness, we also mention here a third type of learning, that is, “reinforcement learning”, in which the model learns to independently reach a goal within a given environment by trial and error, reinforced by operator chosen rewards or penalties resulting from the result of these decisions. For this reason, techniques have been developed to train on unlabeled images, termed “unsupervised learning”. This labeling may in itself be a formidable task, adding to the computational demand. The approach in which the model is trained on a set of images that are labeled by their respective categories is termed “supervised learning”. This model is subsequently applied on new, unseen images. These expert-defined features are fed into the machine learning classification algorithms to create a model that learns the best mapping between the features and the different categories. Therefore, these image processing techniques require expertise in the specific discipline to identify the important features characterizing the categories, and to then extract them from the images. This leads to a loss of information of the neighborhood of a given pixel, and in some cases to an extremely inefficient use of computer resources. When used for image processing, traditional machine learning algorithms analyze images either by flattening the data to 1D vectors, or by extracting features one-by-one and transforming them to 1D vectors. The segmentation can be performed as a “semantic segmentation”, which classifies image pixels into specific categories, while “instance segmentation” additionally segments each object in the image. In many biological applications, nucleus identification (segmentation) is used as a good reference for image analysis approaches, that is, cell counting or tracking, and protein localization. For example, in medical image diagnosis, the segmentation of organs allows for the quantification of their volume and shape. This involves dividing an image into several parts, sets of pixels, to locate objects and identify specific structures and boundaries, namely “pixel classification”. An even finer distinction is “segmentation”. “Detection” of objects in an image is usually performed by defining bounding boxes around the objects of interest, outputting the spatial coordinates defining locations and sizes of bounding boxes, while “localization” is a specific case of detection of a single object in an image. More advanced tasks in computer vision are object localization and detection. There could be many different classes in a data set: Red blood cells in wide-field images can be classified into ten different classes based on morphological differences. “Classifying” means assigning the image to a specific category, such as dog/cat for kind of animal, healthy/diseased for cells, particular orientation/configuration for an adsorbed molecule. Ĭomputer vision, which can be used to derive complex information from digital images and videos (equivalent and even superior to expert humans), frequently relies on machine learning, and image classification is one of the most basic tasks. Machine learning is thus an umbrella term for many different types of learning. In this review our major concern is with images, which are most relevant to certain aspects of machine learning as will be described below. This broad definition includes a variety of tasks including, but not limited to, classification, regression (prediction of quantitative data values), translation (for instance of languages), anomaly detection, de-noising, clustering (grouping similar objects together), and data generation. It was defined by Arthur Samuel of IBM in 1959 as the “Field of study that gives computers the ability to learn without being explicitly programmed”. Machine learning is a subfield of artificial intelligence. Review Introduction: traditional machine learning vs deep learning
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