Artificial Intelligence and Machine Learning for Digital Pathology : State-of-the-Art and Future Challenges

Andreas Holzinger

ISBN 10: 3030504018 / ISBN 13: 9783030504014

Editore: Springer, Springer, 2020

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9783030504014

Dati bibliografici

• Titolo: Artificial Intelligence and Machine Learning for Digital Pathology : State-of-the-Art and Future Challenges
• Editore: Springer, Springer
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 3030504018
• ISBN 13: 9783030504014
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 20 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 20 millimetri di profondità
• Peso: 540 grammi

Descrizione articolo

Riassunto

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.