2 research outputs found
Learn to Estimate Labels Uncertainty for Quality Assurance
Deep Learning sets the state-of-the-art in many challenging tasks showing
outstanding performance in a broad range of applications. Despite its success,
it still lacks robustness hindering its adoption in medical applications.
Modeling uncertainty, through Bayesian Inference and Monte-Carlo dropout, has
been successfully introduced for better understanding the underlying deep
learning models. Yet, another important source of uncertainty, coming from the
inter-observer variability, has not been thoroughly addressed in the
literature. In this paper, we introduce labels uncertainty which better suits
medical applications and show that modeling such uncertainty together with
epistemic uncertainty is of high interest for quality control and referral
systems.Comment: Under Revie
Uncertainty-based graph convolutional networks for organ segmentation refinement
Organ segmentation in CT volumes is an important pre-processing step in many
computer assisted intervention and diagnosis methods. In recent years,
convolutional neural networks have dominated the state of the art in this task.
However, since this problem presents a challenging environment due to high
variability in the organ's shape and similarity between tissues, the generation
of false negative and false positive regions in the output segmentation is a
common issue. Recent works have shown that the uncertainty analysis of the
model can provide us with useful information about potential errors in the
segmentation. In this context, we proposed a segmentation refinement method
based on uncertainty analysis and graph convolutional networks. We employ the
uncertainty levels of the convolutional network in a particular input volume to
formulate a semi-supervised graph learning problem that is solved by training a
graph convolutional network. To test our method we refine the initial output of
a 2D U-Net. We validate our framework with the NIH pancreas dataset and the
spleen dataset of the medical segmentation decathlon. We show that our method
outperforms the state-of-the art CRF refinement method by improving the dice
score by 1% for the pancreas and 2% for spleen, with respect to the original
U-Net's prediction. Finally, we discuss the results and current limitations of
the model for future work in this research direction. For reproducibility
purposes, we make our code publicly available.Comment: Accepted at MIDL 202