  Deep learning has proven to be an essential tool for medical image analysis.
However, the need for accurately labeled input data, often requiring time- and
labor-intensive annotation by experts, is a major limitation to the use of deep
learning. One solution to this challenge is to allow for use of coarse or noisy
labels, which could permit more efficient and scalable labeling of images. In
this work, we develop a lopsided loss function based on entropy regularization
that assumes the existence of a nontrivial false negative rate in the target
annotations. Starting with a carefully annotated brain metastasis lesion
dataset, we simulate data with false negatives by (1) randomly censoring the
annotated lesions and (2) systematically censoring the smallest lesions. The
latter better models true physician error because smaller lesions are harder to
notice than the larger ones. Even with a simulated false negative rate as high
as 50%, applying our loss function to randomly censored data preserves maximum
sensitivity at 97% of the baseline with uncensored training data, compared to
just 10% for a standard loss function. For the size-based censorship,
performance is restored from 17% with the current standard to 88% with our
lopsided bootstrap loss. Our work will enable more efficient scaling of the
image labeling process, in parallel with other approaches on creating more
efficient user interfaces and tools for annotation.
