We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

Email address: Olivier.Commowick@inria.fr (Olivier Commowick) Preprint submitted to Nature Scientific Reports July 12, 2018 . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/367557 doi: bioRxiv preprint first posted online Jul. 13, 2018; We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, . . . ), are still trailing human expertise on both detection and delineation criteria.In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

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Introduction: Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods: To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes.Results: Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion: ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.Keywords Breast cancer early diagnosis, Thermographic images, Mammary thermography, Artificial neural networks, Extreme learning machines.This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.How to cite this article: Santana MA, Pereira JMS, Silva FL, Lima NM, Sousa FN, Arruda GMS, Lima RCF, Silva WWA, Santos WP. Breast cancer diagnosis based on mammary thermography and extreme learning machines. Res Biomed Eng. 2018; 34(1):45-53.

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