Deep Learning in Medical Image Analysis: Challenges and Applications (Advances in Experimental Medicine and Biology, 1213)

Price: $219.99 – $129.39
(as of Dec 25,2024 00:07:18 UTC – Details)

Publisher ‏ : ‎ Springer; 1st ed. 2020 edition (February 7, 2020)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 189 pages
ISBN-10 ‏ : ‎ 303033127X
ISBN-13 ‏ : ‎ 978-3030331276
Item Weight ‏ : ‎ 1.22 pounds
Dimensions ‏ : ‎ 7 x 0.5 x 10 inches

Deep Learning in Medical Image Analysis: Challenges and Applications (Advances in Experimental Medicine and Biology, 1213)

Medical image analysis plays a crucial role in the diagnosis and treatment of various diseases. With the advancements in deep learning techniques, there has been a significant improvement in the accuracy and efficiency of medical image analysis. Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown great promise in extracting meaningful information from medical images.

However, despite the potential benefits of deep learning in medical image analysis, there are several challenges that researchers and practitioners face. One of the main challenges is the lack of annotated data sets for training deep learning models. Annotated data sets are essential for training deep learning algorithms, but they can be time-consuming and expensive to create.

Another challenge is the interpretability of deep learning models. Deep learning models are often considered to be black boxes, making it difficult to understand how they arrive at a particular decision. This lack of interpretability can be a barrier to the adoption of deep learning in clinical settings.

Despite these challenges, deep learning has been successfully applied to a wide range of medical image analysis tasks, including tumor detection, organ segmentation, and disease classification. Deep learning models have shown superior performance compared to traditional machine learning algorithms in many cases.

In the book “Deep Learning in Medical Image Analysis: Challenges and Applications” (Advances in Experimental Medicine and Biology, 1213), leading researchers in the field discuss the latest advances in deep learning for medical image analysis. The book covers topics such as data augmentation, transfer learning, and adversarial training, providing valuable insights for researchers and practitioners interested in applying deep learning to medical image analysis.

Overall, deep learning holds great potential for revolutionizing medical image analysis, but researchers must overcome various challenges to fully harness its power. The book “Deep Learning in Medical Image Analysis: Challenges and Applications” provides a comprehensive overview of the current state of the art and future directions in this exciting field.
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