B. Kann

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom B. Kann trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Tian Guo
  2. K. Polyak
  3. Eric J. Topol
  4. Michael E. Porter
  5. Tao Lin

Papers from B. Kann's perspective

Ordered by how strongly the researchers B. Kann trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Deep Learning
  2. International evaluation of an AI system for breast cancer screening
  3. High-performance medicine: the convergence of human and artificial intelligence
  4. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository (Journal of digital imaging)
  5. Challenges to the Reproducibility of Machine Learning Models in Health Care (Journal of the American Medical Association (JAMA))
  6. Intra-tumour heterogeneity: a looking glass for cancer? (Nature Reviews. Cancer)
  7. Improving the accuracy of medical diagnosis with causal machine learning
  8. Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging (Clinical Cancer Research)
  9. External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms.
  10. Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

Their papers