Self Supervised Learning for Object Localisation in 3D Tomographic Images.
Year: 2020
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Yaroslav Zharov, Alexey Ershov, Tilo Baumbach
Institution: Karlsruhe Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3099827326