Clearing noisy annotations for computed tomography imaging

One of the problems on the way to successful implementation of neural networks is the quality of annotation. For instance, different annotators can annotate images in a different way and very often their decisions do not match exactly and in extreme cases are even mutually exclusive which results in...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2018-07
Main Authors: Khudorozhkov, Roman, Koryagin, Alexander, Kozhevin, Alexey
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Clearing
Computation
Computed tomography
Confidence intervals
Image annotation
Image classification
Image segmentation
Medical imaging
Neural networks
Nodules
Statistical analysis
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:One of the problems on the way to successful implementation of neural networks is the quality of annotation. For instance, different annotators can annotate images in a different way and very often their decisions do not match exactly and in extreme cases are even mutually exclusive which results in noisy annotations and, consequently, inaccurate predictions. To avoid that problem in the task of computed tomography (CT) imaging segmentation we propose a clearing algorithm for annotations. It consists of 3 stages: - annotators scoring, which assigns a higher confidence level to better annotators; - nodules scoring, which assigns a higher confidence level to nodules confirmed by good annotators; - nodules merging, which aggregates annotations according to nodules confidence. In general, the algorithm can be applied to many different tasks (namely, binary and multi-class semantic segmentation, and also with trivial adjustments to classification and regression) where there are several annotators labeling each image.
ISSN:2331-8422