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Self Supervised Learning for Object Localisation in 3D Tomographic Images

2020-11-06 13:34:00
Yaroslav Zharov, Alexey Ershov, Tilo Baumbach
arXiv_CV
       
Abstract

While a lot of work is dedicated to self-supervised learning, most of it is dealing with 2D images of natural scenes and objects. In this paper, we focus on \textit{volumetric} images obtained by means of the X-Ray Computed Tomography (CT). We describe two pretext training tasks which are designed taking into account the specific properties of volumetric data. We propose two ways to transfer a trained network to the downstream task of object localization with a zero amount of manual markup. Despite its simplicity, the proposed method shows its applicability to practical tasks of object localization and data reduction.

Abstract (translated)

URL

https://arxiv.org/abs/2011.03353

PDF

https://arxiv.org/pdf/2011.03353.pdf

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