1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
Author(s) -
Geert Litjens,
Péter Bándi,
Babak Ehteshami Bejnordi,
Oscar Geessink,
Maschenka Balkenhol,
Peter Bult,
Altuna Halilović,
Meyke Hermsen,
Rob van de Loo,
Rob Vogels,
Quirine F. Manson,
Nikolas Stathonikos,
Alexi Baidoshvili,
Paul van Diest,
Carla Wauters,
Marcory van Dijk,
Jeroen van der Laak
Publication year - 2018
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giy065
Subject(s) - breast cancer , sentinel lymph node , medicine , lymph node , pathology , oncology , cancer
The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed.
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