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2 Annals of Computer Science and Information Systems

2 2018

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Dataset enhancement in hair follicle detection: ESENSEI challenge

Jakubik J.

Annals of Computer Science and Information Systems

2018

Vol. 16

19--22

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.

An ensemble of Deep Convolutional Neural Networks for Marking Hair Follicles on Microscopic Images

Podlodowski Ł., Roziewski Sz., Nurzyński M.

Annals of Computer Science and Information Systems

2018

Vol. 16

23--28

This paper presents an application of a Convolutional Neural Network as a solution for a task associated with ESENSEI Challenge: Marking Hair Follicles on Microscopic Images. As we show in this paper quality of classification results could be improved not only by changing architecture but also by ensemble networks. In this paper, we present two solutions for the task, the first one based on benchmark convolutional neural network, and the second one, an ensemble of VGG-16 networks. Presented models took first and third places in the final competition leaderboard.