Challenge #1: Verification of outcome
Challenge #2: Big Data
Challenge #3: Tools for digitalization
Challenge #4: High complexity of histological images
Challenge #5: High dimensionality of pathology diagnostic problems
Challenge #6: Pathologist as the gold standard/ ground truth
Challenge #7: Affordability of computational power and storage space
Challenge #8: Generalizability
Opportunity #1: Powerful Modeling
Opportunity #2: Data augmentation for not enough data
Opportunity #3: Visualization
Opportunity #4: Fast assistantship
Opportunity #5: Ability to deal with complex biomarkers
Opportunity #6: Improved Efficiency and Personalized Treatments
Opportunity #7: Time Saving
Opportunity #8: Reduce Errors
As the digital pathology market grows, facilities that rely on digital pathology will start using artificial intelligence (AI) to assist. AI could help health professionals cope with the gigantic quantities of data. However, this advancement creates some challenges and opportunities. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber‐security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field.
- Kayla Matthews, Ways Artificial Intelligence Is Transforming Digital Pathology, hitconsultant, 2019
- Anil V. Parwani, Next generation diagnostic pathology : use of digital pathology and artificial intelligence tools to augment a pathological diagnosis
, diagnosticpathology, biomedcentral, 2019
- Peter Regitnig, Heimo Müller, Andreas Holzinger, Expectations of Artificial Intelligence for Pathology, Springer, 2020
- Andreas Holzinger, Prof. Dr. Randy Goebel, Prof. Michael Mengel, Heimo Müller, Artificial Intelligence and Machine Learning for Digital Pathology, springerprofessional.de, 2020
- What is the current role of Artificial Intelligence in digital pathology?, Digital Pathology Place,
- Wen, Si, et al. “Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images.”AMIA Summits on Translational Science Proceedings 2017 (2018): 227.
- Shiraishi, Junji, et al. “Computer-aided diagnosis and artificial intelligence in clinical imaging.”Seminars in nuclear medicine. Vol. 41. No. 6. WB Saunders, 2011
- Ralf Huss, Why and how should digital pathology be implemented into clinical practice?, DIGITAL PATHOLOGY PLACE
- MICHELLE DOTZERT, Integrating Artificial Intelligence with Digital Pathology, Lab Manager, 2020
- Abels E, et al. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the digital pathology association. J Pathol. 2019;249(3):286–94.
- فایلهای پروژه به صورت کامل پس از خرید فایل بلافاصله در اختیار شما قرار خواهد گرفت.
درصورتیکه این پروژه دقیقا مطابق خواسته شما نمی باشد، با کلیک بر روی کلید زیر پروژه دلخواه خود را سفارش دهید.