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Deep Learning from Label Proportions for Emphysema Quantification

Abstract. We propose an end-to-end deep learning method that learns
to estimate emphysema extent from proportions of the diseased tissue.
These proportions were visually estimated by experts using a standard
grading system, in which grades correspond to intervals (label example:
1-5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss
is designed to learn with intervals. Thus, during training, our network
learns to segment the diseased tissue such that its proportions fit the
ground truth intervals. Our architecture and loss combined improve the
performance substantially (8% ICC) compared to a more conventional
regression network. We outperform traditional lung densitometry and
two recently published methods for emphysema quantification by a large
margin (at least 7% AUC and 15% ICC), and achieve near-human-level
performance. Moreover, our method generates emphysema segmentations
that predict the spatial distribution of emphysema at human level. Download Learning from Label Proportions for Emphysema Quantification

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