Radiology report analysis provides valuable information that can aid with
public health initiatives, and has been attracting increasing attention from
the research community. In this work, we present a novel insight that the
structure of a radiology report (namely, the Findings and Impression sections)
offers different views of a radiology scan. Based on this intuition, we further
propose a co-training approach, where two machine learning models are built
upon the Findings and Impression sections, respectively, and use each other's
information to boost performance with massive unlabeled data in a
semi-supervised manner. We conducted experiments in a public health
surveillance study, and results show that our co-training approach is able to
improve performance using the dual views and surpass competing supervised and
semi-supervised methods.

放射学报告分析可为公共卫生倡议提供有价值的信息，并且吸引了研究界越来越多的关注。本研究提出了一种新的见解，即放射学报告的结构（即发现和印象部分）提供了对放射学扫描的不同视角。基于这个直觉，我们进一步提出了一种共同训练方法，在发现部分和印象部分分别构建两个机器学习模型，并使用彼此的信息以半监督的方式利用海量未标记的数据来提高性能。我们在公共卫生监测研究中进行了实验，结果表明我们的共同训练方法能够利用双重视角提高性能，并超越竞争的监督和半监督方法。

一种双视图方法用于通过共训练对放射学报告进行分类

A Dual-View Approach to Classifying Radiology Reports by Co-Training

We consider the problem of unsupervised domain adaptation in semantic
segmentation. The key in this campaign consists in reducing the domain shift,
i.e., enforcing the data distributions of the two domains to be similar. A
popular strategy is to align the marginal distribution in the feature space
through adversarial learning. However, this global alignment strategy does not
consider the local category-level feature distribution. A possible consequence
of the global movement is that some categories which are originally well
aligned between the source and target may be incorrectly mapped. To address
this problem, this paper introduces a category-level adversarial network,
aiming to enforce local semantic consistency during the trend of global
alignment. Our idea is to take a close look at the category-level data
distribution and align each class with an adaptive adversarial loss.
Specifically, we reduce the weight of the adversarial loss for category-level
aligned features while increasing the adversarial force for those poorly
aligned. In this process, we decide how well a feature is category-level
aligned between source and target by a co-training approach. In two domain
adaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, we
validate that the proposed method matches the state of the art in segmentation
accuracy.

本文提出了一种基于分类级别对抗网络和协同训练方法的非监督域自适应语义分割模型，通过加强对局部语义一致性的控制来优化全局对齐策略，取得了与当前领先技术水平相当的分割精度。实验任务包括 GTA5->Cityscapes 和 SYNTHIA -> Cityscapes 两种不同的领域自适应任务。