Название: Deep Learners and Deep Learner Descriptors for Medical Applications Автор: Loris Nanni, Sheryl Brahnam
Издательство: Springer
Год: 2020
Страниц: 286
Язык: английский
Формат: pdf (true), epub
Размер: 51.4 MB
Deep Learning (DL) is one of the best-performing approaches in Artificial Intelligence (AI), a field that was revolutionized when it was first proposed. The main feature of Deep Learning is its layered structure, with the different layers forming a hierarchy of processing stages ranging from low-level (layers closer to the input), where more generalizable information or descriptors are discovered, to high-level analysis (layers further away from the input), where information flowing through the network represents high-level concepts. What this means is that every layer adds a certain level of abstraction to the overall representation. Considering medical image interpretation, DL analyzes data through several stages: at a lower level, small image patches are considered, leading to features like edges and texture. Such low-level descriptors are then combined to build more complex representations.
Recent literature presents a wide variety of articles related to the applications of DL, especially as it pertains to Convolutional Neural Networks (CNN), one of the most powerful deep learners for vision tasks. Broadly, there are five methods for using CNN: (1) training a CNN from scratch using data preprocessing, augmentations, and selection to solve any imbalances or insufficiencies in the data; (2) transfer learning from a pretrained CNN as a complementary feature extractor, where the learned features (sometimes combined with advanced handcrafted image features, such as Local Binary Patterns (LBP) and its variants) are trained on other classifiers, such as the Support Vector Machine (SVM); (3) fine-tuning one or more pretrained CNNs on a novel dataset; (4) fusing different CNN architectures; and (5) combining many of the above methods to generate more elaborate ensembles. All five techniques are exploited in medical applications, texture analysis, and biomedical image and sound processing.
The objective of this book is to bring together key researchers working with DL, as described above, on different medical applications. The majority of chapters in this book focus on CNN and medical images; however, chapters on sound as how DL relates to medical sound applications are also included.
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