Self Supervised Learning for Object Localisation in 3D Tomographic Images
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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 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.
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