  Chest X-rays are an important and accessible clinical imaging tool for the
detection of many thoracic diseases. Over the past decade, deep learning, with
a focus on the convolutional neural network (CNN), has become the most powerful
computer-aided diagnosis technology for improving disease identification
performance. However, training an effective and robust deep CNN usually
requires a large amount of data with high annotation quality. For chest X-ray
imaging, annotating large-scale data requires professional domain knowledge and
is time-consuming. Thus, existing public chest X-ray datasets usually adopt
language pattern based methods to automatically mine labels from reports.
However, this results in label uncertainty and inconsistency. In this paper, we
propose many-to-one distribution learning (MODL) and K-nearest neighbor
smoothing (KNNS) methods from two perspectives to improve a single model's
disease identification performance, rather than focusing on an ensemble of
models. MODL integrates multiple models to obtain a soft label distribution for
optimizing the single target model, which can reduce the effects of original
label uncertainty. Moreover, KNNS aims to enhance the robustness of the target
model to provide consistent predictions on images with similar medical
findings. Extensive experiments on the public NIH Chest X-ray and CheXpert
datasets show that our model achieves consistent improvements over the
state-of-the-art methods.
