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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. |
Why? - build a strategic, smart and strong analytics capability to transform your institution and ensure a future proof competitive advantage. This type of transformation impacts top-line growth—such as those related to institutional transformation and data utilization—as well as productivity and performance. This discipline includes: Agile and rapid prototyping. Analytics capability assessment and transformation. Remember the conviction: #Analyticship:#BI,#ML,#AI,#BigData,#Analytics,#HiEd:
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Monday, August 10, 2020
Artificial Intelligence and Machine Learning for Digital Pathology...
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