Literature DB >> 27761297

Erratum: Antibody supervised deep learning for quantification of tumor infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.

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Abstract

[This corrects the article on p. 38 in vol. 7, PMID: 27688929.].

Entities:  

Year:  2016        PMID: 27761297      PMCID: PMC5070030          DOI: 10.4103/2153-3539.191031

Source DB:  PubMed          Journal:  J Pathol Inform


In the article titled, “Antibody supervised deep learning for quantification of tumor infiltrating immune cells in hematoxylin and eosin stained breast cancer samples” published as elocation ID 38, in vol. 7 of Journal of Pathology Informatics[1], the first sentence in the second last paragraph under the “Conclusions” section is written incorrectly as “Studies with LBP-based features have shown good agreement with human experts in classification of tissue morphologies, Linder et al. 2014 and Turkki et al. 2015[9,37] and they are broadly applied in medical image analysis.[38]” instead of “Studies with LBP-based features have shown good agreement with human experts in classification of tissue morphologies.[9,37] and they are broadly applied in medical image analysis.[38]” In the article, the sentence under the section “Financial Support and Sponsorship” are missing and they are as follows: “The present study has received funding from following sources: “European Advanced Translational Research Infra Structure in Medicine” (EATRIS), Academy of Finland, the Innovative Medicines Initiative (IMI), PREDECT, Sigrid Juselius Foundation and the Doctoral Program in Biomedicine in the Doctoral School in Health Science (DSHealth), University of Helsinki.” In the article, the section “Acknowledgements” is missing and it should appear as follows: “Acknowledgements: Authors acknowledge Mikko Mäyränpää for contributing with visual assessment of the test images. Additionally, we acknowledge Katja Välimäki, Antonio Ribeiro and Leena Saikko for technical work. Sami Blom is acknowledged for valuable discussions regarding the study.”
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1.  Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.

Authors:  Riku Turkki; Nina Linder; Panu E Kovanen; Teijo Pellinen; Johan Lundin
Journal:  J Pathol Inform       Date:  2016-09-01
  1 in total

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