Who Cited It

Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology

2019 · Nature Reviews Clinical Oncology · 1,680 citations · 1 from inside this corpus

Kaustav Bera, Kurt A. Schalper, David L. Rimm, Vamsidhar Velcheti, Anant Madabhushi

No abstract in the source record.

Artificial intelligence in digital pathology — new tools for diagnosis and precision onco… (2019)Artificial intelligence in di…Long Short-Term Memory (1997)Long Short-Term MemoryLearning representations by back-propagating errors (1986)Learning representations by b…Reducing the Dimensionality of Data with Neural Networks (2006)Reducing the Dimensionality o…Stop explaining black box machine learning models for high stakes decisions and use inter… (2019)Stop explaining black box mac…The Handbook of brain theory and neural networks (1996)The Handbook of brain theory …Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases … (2017)Diagnostic Assessment of Deep…Evolving artificial neural networks (1999)Evolving artificial neural ne…Classification and mutation prediction from non–small cell lung cancer histopathology ima… (2018)Classification and mutation p…Deep Learning: Methods and Applications (2014)Deep Learning: Methods and Ap…On the Opportunities and Risks of Foundation Models (2021)On the Opportunities and Risk…
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What this paper cites, inside the corpus

PaperYearCited
Long Short-Term Memory1997101,359
Learning representations by back-propagating errors198631,688
Reducing the Dimensionality of Data with Neural Networks200621,247
Stop explaining black box machine learning models for high stakes decisions and use inter…20199,924
The Handbook of brain theory and neural networks19963,915
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases …20173,388
Evolving artificial neural networks19993,011
Classification and mutation prediction from non–small cell lung cancer histopathology ima…20182,987
Deep Learning: Methods and Applications20141,801

What cites it, inside the corpus

PaperYearCited
On the Opportunities and Risks of Foundation Models20212,265

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Topics

AI in cancer detectionComputer Science
Radiomics and Machine Learning in Medical ImagingMedicine
Cancer Genomics and DiagnosticsBiochemistry, Genetics and Molecular Biology

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complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports5 author record(s) attached.
  • supports160 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:56+00:00.

sha256 7bc26169e93749d1…