Learning Image Labels On-the-fly for Training Robust Classification
Models
- URL: http://arxiv.org/abs/2009.10325v2
- Date: Fri, 2 Oct 2020 04:35:55 GMT
- Title: Learning Image Labels On-the-fly for Training Robust Classification
Models
- Authors: Xiaosong Wang, Ziyue Xu, Dong Yang, Leo Tam, Holger Roth, Daguang Xu
- Abstract summary: We show how noisy annotations (e.g., from different algorithm-based labelers) can be utilized together and mutually benefit the learning of classification tasks.
A meta-training based label-sampling module is designed to attend the labels that benefit the model learning the most through additional back-propagation processes.
- Score: 13.669654965671604
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Current deep learning paradigms largely benefit from the tremendous amount of
annotated data. However, the quality of the annotations often varies among
labelers. Multi-observer studies have been conducted to study these annotation
variances (by labeling the same data for multiple times) and its effects on
critical applications like medical image analysis. This process indeed adds an
extra burden to the already tedious annotation work that usually requires
professional training and expertise in the specific domains. On the other hand,
automated annotation methods based on NLP algorithms have recently shown
promise as a reasonable alternative, relying on the existing diagnostic reports
of those images that are widely available in the clinical system. Compared to
human labelers, different algorithms provide labels with varying qualities that
are even noisier. In this paper, we show how noisy annotations (e.g., from
different algorithm-based labelers) can be utilized together and mutually
benefit the learning of classification tasks. Specifically, the concept of
attention-on-label is introduced to sample better label sets on-the-fly as the
training data. A meta-training based label-sampling module is designed to
attend the labels that benefit the model learning the most through additional
back-propagation processes. We apply the attention-on-label scheme on the
classification task of a synthetic noisy CIFAR-10 dataset to prove the concept,
and then demonstrate superior results (3-5% increase on average in multiple
disease classification AUCs) on the chest x-ray images from a hospital-scale
dataset (MIMIC-CXR) and hand-labeled dataset (OpenI) in comparison to regular
training paradigms.
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