No image available
Artificial Intelligence and Machine Learning for Digital Pathology: State-of-the-Art and Future Challenges (Lecture Notes in Computer Science) Paperback - 2020
by Goebel, Randy (Editor) / Holzinger, Andreas (Editor) / Mengel, Michael (Editor) / Müller, Heimo (Editor)
- New
- Paperback
A$228.69
A$29.16
Delivery to USA
Standard delivery: 7 to 14 days
More delivery options
Standard delivery: 7 to 14 days
Ships from Revaluation Books (Devon, United Kingdom)
Details
- Title Artificial Intelligence and Machine Learning for Digital Pathology: State-of-the-Art and Future Challenges (Lecture Notes in Computer Science)
- Author Goebel, Randy (Editor) / Holzinger, Andreas (Editor) / Mengel, Michael (Editor) / Müller, Heimo (Editor)
- Binding Paperback
- Condition New
- Pages 341
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2020
- Features Annotated
- Bookseller's Inventory # x-3030504018
- 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
- Quantity available 2
About Revaluation Books Devon, United Kingdom
Biblio member since 2020
General bookseller of both fiction and non-fiction.
Reader reviews for Artificial Intelligence and Machine Learning for Digital Pathology: State-of-the-Art and Future Challenges (Lecture Notes in Computer Science)
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
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.
