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Categorización de anormalidades cancerígenas en mastografías digitales aplicando aprendizaje profundo

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  • Institute Technological of Misantla
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With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.
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Introduction Breast cancer is the first leading cause of death for women in Brazil as well as in most countries in the world. Due to the relation between the breast density and the risk of breast cancer, in medical practice, the breast density classification is merely visual and dependent on professional experience, making this task very subjective. The purpose of this paper is to investigate image features based on histograms and Haralick texture descriptors so as to separate mammographic images into categories of breast density using an Artificial Neural Network. Methods We used 307 mammographic images from the INbreast digital database, extracting histogram features and texture descriptors of all mammograms and selecting them with the K-means technique. Then, these groups of selected features were used as inputs of an Artificial Neural Network to classify the images automatically into the four categories reported by radiologists. Results An average accuracy of 92.9% was obtained in a few tests using only some of the Haralick texture descriptors. Also, the accuracy rate increased to 98.95% when texture descriptors were mixed with some features based on a histogram. Conclusion Texture descriptors have proven to be better than gray levels features at differentiating the breast densities in mammographic images. From this paper, it was possible to automate the feature selection and the classification with acceptable error rates since the extraction of the features is suitable to the characteristics of the images involving the problem.
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Influencia de la mamografía digital en la detección y manejo de microcalcificaciones. Radiología: Publicación oficial de la Sociedad Española de
  • M Melladoa
  • M Osab
  • A Murillo
Melladoa, M., Osab, M., Murillo, A.: Influencia de la mamografía digital en la detección y manejo de microcalcificaciones. Radiología: Publicación oficial de la Sociedad Española de Radiología Médica 55(2), pp.142-147 (2013)
Mass segmentation in digital mammograms
  • M Carreras
  • M Martínez
  • K Rosas
Carreras, M., Martínez, M., Rosas, K.: Mass segmentation in digital mammograms. Ambient Intelligence for Health 9456(1), pp. 110-115 (2015)
Método Heurístico para el Diagnóstico de Cáncer de Mama basado en Minería de Datos
  • S Camacho
Camacho, S.: Método Heurístico para el Diagnóstico de Cáncer de Mama basado en Minería de Datos. Revista PGI -Investigación, Científica y Tecnología 1, pp. 97-101 (2014)
Breast mass classification from mammograms using deep convolutional neural networks
  • D Lévy
  • F González
Lévy, D., González, F.: Breast mass classification from mammograms using deep convolutional neural networks. In: CoRR (2016)
INEGI: Estadísticas a propósito del
INEGI: Estadísticas a propósito del. Día mundial contra el cáncer, http://www.beta.inegi. org.mx/contenidos/saladeprensa/ aproposito/2018/ cancer2018_Nal (2018)