Artificial Intelligence and Machine Learning for Digital Pathology: State-Of-The-Art and Future Challenges Paperback - 2020
by Andreas Holzinger (Editor); Randy Goebel (Editor); Michael Mengel (Editor)
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From the publisher
From the rear cover
Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support.
Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ''fit-for-purpose'' samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.
Details
- Title Artificial Intelligence and Machine Learning for Digital Pathology: State-Of-The-Art and Future Challenges
- Author Andreas Holzinger (Editor); Randy Goebel (Editor); Michael Mengel (Editor)
- Binding Paperback
- Pages 341
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2020-06-21
- Features Annotated
- ISBN 9783030504014 / 3030504018
- Weight 1.1 lbs (0.50 kg)
- Dimensions 9.1 x 7.7 x 0.6 in (23.11 x 19.56 x 1.52 cm)
- Category Computers - General Information
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Artificial Intelligence and Machine Learning for Digital Pathology: State-of-the-Art and Future Challenges (Lecture Notes in Computer Science)
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