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Dual Stage Normalization Approach Towards Classification of Breast Cancer
M. A. Aswathy,Jagannath Mohan +1 more
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The objective of the study was to establish a histopathological basis for the prognosis of breast cancer in women with a history of atypical mastectomy and establish a standard of care for such cancer.Abstract:
Breast cancer is a major concern among women that causes high risk of death. Early diagnosis of such cancer becomes challenging due to alterations in the color of the histopathological breast image...read more
Citations
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Multi-Deep CNN based Experimentations for Early Diagnosis of Breast Cancer
TL;DR: In this article , the authors examined a modern Computer-Aided Diagnosis (CAD) framework that uses DL to extract features and classify them for aiding radiologists in breast cancer diagnosis.
Journal ArticleDOI
A stain color normalization with robust dictionary learning for breast cancer histological images processing
Thaina Aparecida Azevedo Tosta,A. D. Freitas,Paulo Rogério de Faria,Leandro Alves Neves,Alessandro Santana Martins,Marcelo Zanchetta do Nascimento +5 more
TL;DR: In this paper , the color appearance matrices and density maps of the stain were estimated to improve the color estimation of histological images, and a method for normalizing hematoxylin and eosin-stained histology images was proposed.
References
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Lindsey A. Torre,Freddie Bray,Rebecca L. Siegel,Jacques Ferlay,Joannie Lortet-Tieulent,Ahmedin Jemal +5 more
TL;DR: A substantial portion of cancer cases and deaths could be prevented by broadly applying effective prevention measures, such as tobacco control, vaccination, and the use of early detection tests.
Journal ArticleDOI
Cancer statistics, 2016
TL;DR: Overall cancer incidence trends are stable in women, but declining by 3.1% per year in men, much of which is because of recent rapid declines in prostate cancer diagnoses, and brain cancer has surpassed leukemia as the leading cause of cancer death among children and adolescents.
Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
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Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Geert Litjens,Clara I. Sánchez,Nadya Timofeeva,Meyke Hermsen,Iris D. Nagtegaal,Iringo Kovacs,Christina Hulsbergen van de Kaa,Peter Bult,Bram van Ginneken,Jeroen van der Laak +9 more
TL;DR: It is found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30–40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention.
Journal ArticleDOI
Classification of breast cancer histology images using Convolutional Neural Networks
Teresa Araújo,Guilherme Aresta,Eduardo Castro,José Rouco,Paulo Aguiar,Catarina Eloy,António Polónia,Aurélio Campilho +7 more
TL;DR: A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed and the sensitivity of the method for cancer cases is 95.6%.