{"id":837,"date":"2019-05-21T16:10:57","date_gmt":"2019-05-21T14:10:57","guid":{"rendered":"https:\/\/pmf-research.eu\/artificial-intelligence-in-medical-imaging\/"},"modified":"2022-08-24T12:42:39","modified_gmt":"2022-08-24T10:42:39","slug":"artificial-intelligence-in-medical-imaging","status":"publish","type":"post","link":"https:\/\/pmf-research.eu\/en\/artificial-intelligence-in-medical-imaging\/","title":{"rendered":"ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tA recent report by the Global Market Insights<\/a> reveals that the market of artificial intelligence applied to health care<\/strong> in 2016 reached 750 million dollars and it is expected to grow by 2024 with an annual growth rate of 40% per year.\r\n\r\nThe field of artificial intelligence<\/strong> can be represented through a Venn diagram that highlights the conceptual relationships between the different techniques.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Diagramma\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFrom the diagram it is possible to see that deep learning<\/strong> is a special case of representation learning, which in turn is a subset of machine learning<\/strong> and the whole is a sub-division of artificial intelligence<\/strong> technologies. The term machine learning<\/strong> refers to a set of techniques and approaches aimed at solving certain types of problems that require the algorithm to learn from a certain set of data. The study of machine learning algorithms is currently a very active field of research<\/strong>, which has registered many successes in the field of image recognition and understanding of spoken language. In these areas, in fact, there are very difficult problems to solve with “hard coded<\/strong>” programmes, whose functioning does not involve training. In this approach, the programmes subjected to training learn a hierarchy of data concepts (known as data representation). When we learn representations from other representations, we talk about a profound stratification of learning: deep learning<\/strong>.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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APPLICATIONS OF NEURAL NETWORKS IN MEDICAL IMAGING<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIn the health field, artificial intelligence is widely used in medical imaging<\/strong>, as it allows a better characterization and a faster identification of metastases that can improve the outcomes of therapies. In particular, deep learning techniques<\/strong> are mostly used for those aspects related to medical imaging, which will be analysed below.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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CLASSIFICATION OF EXAMS AND IMAGES <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tOne of the first uses of deep learning algorithms<\/strong> in medical imaging is the classification of images. Usually there is a certain number of images as inputs and a single diagnostic variable in the output (presence \/ absence of the disease). As for datasets, they are much more restricted in medical imaging<\/a> than computer vision in general (hundreds \/ thousands of samples against millions). For this reason, the transfer learning technique<\/strong> is often used, which basically consists in pre-training the neural network to circumvent the data training needs of the programme. There are two mostly used transfer learning strategies:\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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