Data-Driven Clinical Decision-Making Using Deep Learning in Imaging (eBook)
XII, 274 Seiten
Springer Nature Singapore (Verlag)
978-981-97-3966-0 (ISBN)
This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.
This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.
| Erscheint lt. Verlag | 13.8.2024 |
|---|---|
| Reihe/Serie | Studies in Big Data |
| Zusatzinfo | XII, 274 p. 105 illus., 92 illus. in color. |
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Medizin / Pharmazie ► Medizinische Fachgebiete ► Radiologie / Bildgebende Verfahren | |
| Studium ► 2. Studienabschnitt (Klinik) ► Anamnese / Körperliche Untersuchung | |
| Technik ► Bauwesen | |
| Schlagworte | Breast Cancer • convolutional neural network • Deep learning • machine learning • Medical Imaging • meta-heuristics • Optimization |
| ISBN-10 | 981-97-3966-7 / 9819739667 |
| ISBN-13 | 978-981-97-3966-0 / 9789819739660 |
| Haben Sie eine Frage zum Produkt? |
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