Despite the success of deep neural networks in chest X-ray (CXR) diagnosis,
supervised learning only allows the prediction of disease classes that were
seen during training. At inference, these networks cannot predict an unseen
disease class. Incorporating a new class requires the collection of labeled
data, which is not a trivial task, especially for less frequently-occurring
diseases. As a result, it becomes inconceivable to build a model that can
diagnose all possible disease classes. Here, we propose a multi-label
generalized zero shot learning (CXR-ML-GZSL) network that can simultaneously
predict multiple seen and unseen diseases in CXR images. Given an input image,
CXR-ML-GZSL learns a visual representation guided by the input's corresponding
semantics extracted from a rich medical text corpus. Towards this ambitious
goal, we propose to map both visual and semantic modalities to a latent feature
space using a novel learning objective. The objective ensures that (i) the most
relevant labels for the query image are ranked higher than irrelevant labels,
(ii) the network learns a visual representation that is aligned with its
semantics in the latent feature space, and (iii) the mapped semantics preserve
their original inter-class representation. The network is end-to-end trainable
and requires no independent pre-training for the offline feature extractor.
Experiments on the NIH Chest X-ray dataset show that our network outperforms
two strong baselines in terms of recall, precision, f1 score, and area under
the receiver operating characteristic curve. Our code is publicly available at:
this https URL

本文提出了一种能够同时预测 CXR 图像中多个已见与未见疾病的多标签广义零样学习 (CXR-ML-GZSL) 网络，借助一个丰富的医学文本语料库提取的语义向量指导图像表示的学习，该网络在 NIH Chest X-ray 数据集上的实验表明其能够优于两个强基线方法。

胸部 X 光片疾病分类的多标签广义零样本学习

Multi-Label Generalized Zero Shot Learning for the Classification of  Disease in Chest Radiographs

Being one of the most common diagnostic imaging tests, chest radiography
requires timely reporting of potential findings in the images. In this paper,
we propose an end-to-end architecture for abnormal chest X-ray identification
using generative adversarial one-class learning. Unlike previous approaches,
our method takes only normal chest X-ray images as input. The architecture is
composed of three deep neural networks, each of which learned by competing
while collaborating among them to model the underlying content structure of the
normal chest X-rays. Given a chest X-ray image in the testing phase, if it is
normal, the learned architecture can well model and reconstruct the content; if
it is abnormal, since the content is unseen in the training phase, the model
would perform poorly in its reconstruction. It thus enables distinguishing
abnormal chest X-rays from normal ones. Quantitative and qualitative
experiments demonstrate the effectiveness and efficiency of our approach, where
an AUC of 0.841 is achieved on the challenging NIH Chest X-ray dataset in a
one-class learning setting, with the potential in reducing the workload for
radiologists.

本文提出一种端到端的基于生成对抗一类学习的异常 X 光识别方法，使用三种深度神经网络建立了正常胸部 X 光图像的内在结构模型，实现了对异常胸部 X 光的准确检测和区分，取得了 0.841 的 AUC 得分。