Meta-Data Construction for Selection of Breast Tissue Biopsy Slides Image Classifier to Identify Ductal Carcinoma

Currently there are large amounts of data available, to obtain useful information, multiple methods have been created to fulfill specific tasks, however, identifying the most appropriate method is often a difficult task. Meta-Learning is presented as an option that can recommend for new data the most appropriate method to perform a particular task based on experience, in which the features of the data and the performance of methods are related, this relationship is known as Meta-Data. Given the continuous increase of patients with breast cancer cases and availability of datasets, the images of slides of breast tissue biopsy to identify Ductal Carcinoma were selected as the object of study. The aim of this work is construction of Meta-Data that allows application of Meta-Learning for selection of the best Ductal Carcinoma identification method in the type of images under study. The proposed methodology presents a performance of the 99.6% accuracy, 99.9% AUC and 99.7% F-measure for Meta-Data Validation.

[1]  Josien P. W. Pluim,et al.  Exploring the Similarity of Medical Imaging Classification Problems , 2017, CVII-STENT/LABELS@MICCAI.

[2]  Nima Khakzad,et al.  Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures , 2015, Reliab. Eng. Syst. Saf..

[3]  Luiz Eduardo Soares de Oliveira,et al.  A Dataset for Breast Cancer Histopathological Image Classification , 2016, IEEE Transactions on Biomedical Engineering.

[4]  Richard H. Moore,et al.  Current Status of the Digital Database for Screening Mammography , 1998, Digital Mammography / IWDM.

[5]  János Izsák,et al.  A link between ecological diversity indices and measures of biodiversity. , 2000 .

[6]  Arthur Getis,et al.  Point pattern analysis , 1985 .

[7]  Ricardo B. C. Prudêncio,et al.  Cost-Sensitive Measures of Algorithm Similarity for Meta-learning , 2014, BRACIS.

[8]  Mallika Siva Donepudi,et al.  Breast cancer statistics and markers. , 2014, Journal of cancer research and therapeutics.

[9]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[10]  D P Faith,et al.  Phylogenetic pattern and the quantification of organismal biodiversity. , 1994, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[11]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[12]  Pavel Brazdil,et al.  Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue , 2017, Machine Learning.

[13]  M. Akil,et al.  A comparison study between MLP and convolutional neural network models for character recognition , 2017, Commercial + Scientific Sensing and Imaging.

[14]  Catarina Eloy,et al.  Classification of breast cancer histology images using Convolutional Neural Networks , 2017, PloS one.

[15]  Anselmo Cardoso de Paiva,et al.  Diagnosis of Non-Small Cell Lung Cancer Using Phylogenetic Diversity in Radiomics Context , 2018, ICIAR.

[16]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[17]  Péricles B. C. de Miranda,et al.  A Meta-Learning Method to Select Under-Sampling Algorithms for Imbalanced Data Sets , 2016, 2016 5th Brazilian Conference on Intelligent Systems (BRACIS).

[18]  Taghi M. Khoshgoftaar,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[19]  K. R. Clarke,et al.  A taxonomic distinctness index and its statistical properties , 1998 .

[20]  Victor C. M. Leung,et al.  Human action recognition using meta learning for RGB and depth information , 2014, 2014 International Conference on Computing, Networking and Communications (ICNC).

[21]  B. S. Manjunath,et al.  Evaluation and benchmark for biological image segmentation , 2008, 2008 15th IEEE International Conference on Image Processing.

[22]  Linda G. Shapiro,et al.  Automated Diagnosis of Breast Cancer and Pre-invasive Lesions on Digital Whole Slide Images , 2018, ICPRAM.

[23]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  A new data characterization for selecting clustering algorithms using meta-learning , 2019, Inf. Sci..

[24]  K. R. Clarke,et al.  Change in marine communities : an approach to statistical analysis and interpretation , 2001 .