Deep learning in histopathology: the path to the clinic

By J. A. van der Laak, Geert J. S. Litjens, Francesco Ciompi. Nature Medicine, 2021

844 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7d873a9c49d3864709aa762f8740edcdbd7369c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohit Bansal
2. Pranav Rajpurkar
3. Andrew Y. Ng
4. Olivier Gevaert
5. Luke S. Zettlemoyer
6. Jianfeng Gao
7. Percy Liang
8. Richard Socher

In Health Informatics

1. Pranav Rajpurkar

In Radiology, Nuclear Medicine and Imaging

1. Pranav Rajpurkar
2. Andrew Y. Ng
3. Olivier Gevaert

Citations in the corpus

• Multimodal data fusion for cancer biomarker discovery with deep learning (2023, background): “Ongoing improvements in artificial intelligence (AI) and advanced machine learning (ML) techniques have had major impacts on these cancer-imaging ecosystems, especially in diagnostic and prognostic disciplines 17 .”
• Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning (2022, methodology): “For instance, the self-supervised method could leverage the availability of pathology reports that describe diagnoses such as cancer present in histopathology scans 26,35,36 .”