Categorización de anormalidades cancerígenas en mastografías digitales aplicando aprendizaje profundo

  • 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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This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second space whose inputs are (for example) the guesses of the original generalizers when taught with part of the learning set and trying to guess the rest of it, and whose output is (for example) the correct guess. When used with multiple generalizers, stacked generalization can be seen as a more sophisticated version of cross-validation, exploiting a strategy more sophisticated than cross-validation's crude winner-takes-all for combining the individual generalizers. When used with a single generalizer, stacked generalization is a scheme for estimating (and then correcting for) the error of a generalizer which has been trained on a particular learning set and then asked a particular question. After introducing stacked generalization and justifying its use, this paper presents two numerical experiments. The first demonstrates how stacked generalization improves upon a set of separate generalizers for the NETtalk task of translating text to phonemes. The second demonstrates how stacked generalization improves the performance of a single surface-fitter. With the other experimental evidence in the literature, the usual arguments supporting cross-validation, and the abstract justifications presented in this paper, the conclusion is that for almost any real-world generalization problem one should use some version of stacked generalization to minimize the generalization error rate. This paper ends by discussing some of the variations of stacked generalization, and how it touches on other fields like chaos theory.
Although mammography is typically the best method to detect breast cancer, it does not recognize 3–20% of the cancer cases. Mammography has established itself as the most efficient technique for detecting tiny cancerous tumor and micro-calcifications are the most difficult to detect since they are very small (0.1–1.0 mm) and they are almost contrasted against the images background. The main purpose of this paper is to provide a new method for the automatic diagnosis of micro-calcification in digital mammograms. It is based on image mining, and the results show 97.35% accuracy, which is improved than the previous works. Tests are based on the standard images data corpus, MIAS. The practical result of this research is registered as an invention in the Patents and Industrial Property Registration Organization numbered as 83119.
Conference Paper
Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel computer-aided diagnose (CAD) system based on one of the regional deep learning techniques: a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Our proposed YOLO-based CAD system contains four main stages: mammograms preprocessing, feature extraction utilizing multi convolutional deep layers, mass detection with confidence model, and finally mass classification using fully connected neural network (FC-NN). A set of training mammograms with the information of ROI masses and their types are used to train YOLO. The trained YOLO-based CAD system detects the masses and classifies their types into benign or malignant. Our results show that the proposed YOLO-based CAD system detects the mass location with an overall accuracy of 96.33%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 85.52%. Our proposed system seems to be feasible as a CAD system capable of detection and classification at the same time. It also overcomes some challenging breast cancer cases such as the mass existing in the pectoral muscles or dense regions.
Purpose: With novel MRIsequences, high spatiotemporal resolution has become available in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Since benign structures in the breast can show enhancement similar to malignancies in DCE-MRI, characterization of detected lesions is an important problem. The purpose of this study is to develop a computer-aided diagnosis(CADx)system for characterization of breast lesions imaged with high spatiotemporal resolution DCE-MRI.
Conference Paper
This paper presents a methodology for automatic segmentation of masses in digital mammograms based on two principles: thresholding and evolutionary algorithm. As the staring point of the particles of the swarm, we used Otsu. Then, we applied the Particle Swarm Optimization (PSO) to optimize, evolutionarily, the search for the global maximum of the thresholds in order to achieve a better segmentation. After the segmentation stage, we executed a reduction of false positives based on region growing, area filter and Graph Clustering.
We propose a machine learning method for breast cancer data analysis and classification, based on support vector machines (SVM) and particle swarm optimization (PSO). This method uses SVM as a model for supervised learning with the goal of minimizing generalization errors, and PSO as an optimization technique for automatic determination of the best values of two algorithmic parameters of SVM. Its performance in solving classification and recognition problems is experimentally tested for a real-world benchmark dataset. The experimental results are compared to those provided by four other methods using three different objective measures of performance.
We develop abc-logitboost, based on the prior work on abc-boost and robust logitboost. Our extensive experiments on a variety of datasets demonstrate the considerable improvement of abc-logitboost over logitboost and abc-mart.
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. aproposito/2018/ cancer2018_Nal (2018)