Prior-informed learning techniques for macroscopic and microscopic imaging biomarker identification /
Based on the success of artificial intelligence (AI), its use for automated diagnostics of medical image data has become a major focus. Despite excellent results on prediction tasks involving big data, a naïve application of deep learning, i.e., without any prior knowledge of the domain, may not be...
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| Format: | Book |
| Language: | English |
| Language Notes: | In English, with abstracts in English and in German. |
| Published: |
Berlin :
Logos Verlag,
[2023].
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| Subjects: |
| Summary: | Based on the success of artificial intelligence (AI), its use for automated diagnostics of medical image data has become a major focus. Despite excellent results on prediction tasks involving big data, a naïve application of deep learning, i.e., without any prior knowledge of the domain, may not be the optimal solution when there are only small amounts of data for the prediction task at hand. This, however, is often the case in clinical studies and biological experiments. Therefore, it may be beneficial to integrate prior information into the learning technique. With that in mind, this book identifies novel macroscopic and microscopic imaging biomarkers for computed tomography and multiphoton microscopy by developing image processing and prior-informed learning techniques for research in pulmonology, oncology and myology. A spectrum of learning methods is explored, ranging from the traditional, i.e., statistics or classical machine learning with handcrafted features, to the modern, i.e., deep learning and meta-learning, resulting in novel hybrid biomarker systems that seamlessly blend prior knowledge with the power of AI. |
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| Physical Description: | 1 volume (multiple pagings) : illustrations (some color) ; 30 cm. |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 3832557156 9783832557157 |