Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

@article{Bejnordi2017DiagnosticAO,
  title={Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.},
  author={Babak Ehteshami Bejnordi and Mitko Veta and Paul Johannes van Diest and Bram van Ginneken and Nico Karssemeijer and Geert J. S. Litjens and Jeroen A W M van der Laak and Meyke Hermsen and Quirine F. Manson and Maschenka Balkenhol and Oscar Geessink and Nikolaos Stathonikos and Marcory R. C. F. van Dijk and Peter Bult and Francisco Beca and Andrew H. Beck and Dayong Wang and Aditya Khosla and Rishab Gargeya and Humayun Irshad and Aoxiao Zhong and Qi Dou and Quanzheng Li and Hao Chen and Huang-Jing Lin and Pheng Ann Heng and Christian Hass and Elia Bruni and Quincy Wong and Ugur Halici and Mustafa {\"U}mit {\"O}ner and Reng{\"u}l Çetin-Atalay and Matt Berseth and Vitali Khvatkov and Alexei Vylegzhanin and Oren Z. Kraus and Muhammad Shaban and Nasir M. Rajpoot and Ruqayya Awan and Korsuk Sirinukunwattana and Talha Qaiser and Yee-Wah Tsang and David Tellez and Jonas Annuscheit and Peter Hufnagl and Mira Valkonen and Kimmo Kartasalo and Leena Latonen and Pekka Ruusuvuori and Kaisa Liimatainen and Shadi Albarqouni and Bharti Mungal and Ami George and Stefanie Demirci and Nassir Navab and Seiryo Watanabe and Shigeto Seno and Yoichi Takenaka and Hideo Matsuda and Hady Ahmady Phoulady and Vassili Kovalev and Alexander Kalinovsky and Vitali Liauchuk and Gloria Bueno and Maria-Milagro Fern{\'a}ndez-Carrobles and Ismael Serrano and {\'O}scar D{\'e}niz and Daniel Racoceanu and Rui Ven{\^a}ncio},
  journal={JAMA},
  year={2017},
  volume={318 22},
  pages={
          2199-2210
        }
}
Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop… CONTINUE READING
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Multiresolution gray-scale and rotation invariant Machine Learning Detection of Breast Cancer Lymph Node Metastases Original Investigation Research jama.com (Reprinted

  • T Ojala, M Pietikainen, T. Maenpaa
  • JAMA December
  • 2017
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