Dataset enhancement in hair follicle detection: ESENSEI challenge

• Autorzy: Jakubik J.

Identyfikatory

DOI

10.15439/2018F388

• Konferencja: Federated Conference on Computer Science and Information Systems (09-12.09.2018 ; Poznań, Poland)
• Języki publikacji: EN

Abstrakty

EN

In this paper, a solution to ESENSEI data mining challenge concerning the analysis of microscopic hair images is described. The task of the challenge was to detect locations of hair follicles in closeup images of a human scalp. The proposed solution is based on a convolutional neural network architecture. To improve generalization performance, we enhance training and test datasets using image transformations applied to both input and output. The chosen transformations are two axis symmetries and switching axes, all of which are possible to apply regardless of resolution without producing interpolation artifacts. Since these can be combined, 2^3 = 8 possible views of each image can be created to expand both training and test data. We demonstrate the effects of dataset enhancement in both training and classifying on results achievable on the competition dataset. The solution placed 2nd in the final challenge evaluation.

Słowa kluczowe

EN

data mining; microscopic image; hair follicle

PL

eksploracja danych; obraz mikroskopowy; mieszek włosowy

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2018
• Tom: Vol. 16
• Strony: 19--22
• Opis fizyczny: Bibliogr. 7 poz., il., wykr.

Twórcy

autor

Jakubik J.

• Wrocław University of Science and Technology, Faculty of Computer Science and Management, Department of Computational Intelligence Wrocław, Poland

Bibliografia

• [1] LeCun, Yann; Leon Bottou; Yoshua Bengio; Patrick Haffner (1998). "Gradient-based learning applied to document recognition". Proceedings of the IEEE. 86 (11): 2278-2324
• [2] Ciresan, Dan; Meier, Ueli; Schmidhuber, Jurgen (June 2012). "Multicolumn deep neural networks for image classification". 2012 IEEE Conference on Computer Vision and Pattern Recognition.
• [3] Dave Steinkraus; Patrice Simard; Ian Buck (2005). "Using GPUs for Machine Learning Algorithms". 12th International Conference on Document Analysis and Recognition (ICDAR 2005). pp. 1115-1119.
• [4] Goodfellow, Ian, Shlens, Jonathon, Szegedy, Christian. (2014). Explaining and Harnessing Adversarial Examples. arXiv 1412.6572.
• [5] https://knowledgepit.fedcsis.org
• [6] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Deep Residual Learning for Image Recognition, arXiv preprint arXiv:1512.03385, 2015
• [7] M. D. Zeiler, "ADADELTA: An Adaptive Learning Rate Method", arXiv preprint arXiv:1212.5701, 2012.

Uwagi

1. Track: Preface

2. Technical Session: 3rd International Workshop on Artificial Intelligence in Machine Vision and Graphics