On the Uncertain Single-View Depths in Endoscopies
- URL: http://arxiv.org/abs/2112.08906v1
- Date: Thu, 16 Dec 2021 14:24:17 GMT
- Title: On the Uncertain Single-View Depths in Endoscopies
- Authors: Javier Rodr\'iguez-Puigvert, David Recasens, Javier Civera, Rub\'en
Mart\'inez-Cant\'in
- Abstract summary: Estimating depth from endoscopic images is a pre-requisite for a wide set of AI-assisted technologies.
In this paper, we explore for the first time Bayesian deep networks for single-view depth estimation in colonoscopies.
Our specific contribution is two-fold: 1) an exhaustive analysis of Bayesian deep networks for depth estimation in three different datasets, highlighting challenges and conclusions regarding synthetic-to-real domain changes and supervised vs. self-supervised methods; and 2) a novel teacher-student approach to deep depth learning that takes into account the teacher uncertainty.
- Score: 12.779570691818753
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Estimating depth from endoscopic images is a pre-requisite for a wide set of
AI-assisted technologies, namely accurate localization, measurement of tumors,
or identification of non-inspected areas. As the domain specificity of
colonoscopies -- a deformable low-texture environment with fluids, poor
lighting conditions and abrupt sensor motions -- pose challenges to multi-view
approaches, single-view depth learning stands out as a promising line of
research. In this paper, we explore for the first time Bayesian deep networks
for single-view depth estimation in colonoscopies. Their uncertainty
quantification offers great potential for such a critical application area. Our
specific contribution is two-fold: 1) an exhaustive analysis of Bayesian deep
networks for depth estimation in three different datasets, highlighting
challenges and conclusions regarding synthetic-to-real domain changes and
supervised vs. self-supervised methods; and 2) a novel teacher-student approach
to deep depth learning that takes into account the teacher uncertainty.
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