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Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

arXiv 2312.03812 ↗First posted 6 Dec 2023cs.CVcited by 0

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Abstract (arXiv)

This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 229 references that are in the map. First posted on arXiv 6 Dec 2023.

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References · 20

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8 more
  1. 2019Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligencecited by 26
  2. 2022MLOps -- Definitions, Tools and Challengescited by 25
  3. 2019Self-Supervised Similarity Learning for Digital Pathologycited by 22
  4. 2022Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Datacited by 18
  5. 2020Overcoming the limitations of patch-based learning to detect cancer in whole slide imagescited by 15
  6. 2022Recommendations on test datasets for evaluating AI solutions in pathologycited by 8
  7. 2022Rethinking Streaming Machine Learning Evaluationcited by 5
  8. 2022A Falsificationist Account of Artificial Neural Networkscited by 4

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