Automated medical image classification is the key component in intelligent
diagnosis systems. However, most medical image datasets contain plenty of
samples of common diseases and just a handful of rare ones, leading to major
class imbalances. Currently, it is an open problem in intelligent diagnosis to
effectively learn from imbalanced training data. In this paper, we propose a
simple yet effective framework, named \textbf{C}lass \textbf{A}ttention to
\textbf{RE}gions of the lesion (CARE), to handle data imbalance issues by
embedding attention into the training process of \textbf{C}onvolutional
\textbf{N}eural \textbf{N}etworks (CNNs). The proposed attention module helps
CNNs attend to lesion regions of rare diseases, therefore helping CNNs to learn
their characteristics more effectively. In addition, this attention module
works only during the training phase and does not change the architecture of
the original network, so it can be directly combined with any existing CNN
architecture. The CARE framework needs bounding boxes to represent the lesion
regions of rare diseases. To alleviate the need for manual annotation, we
further developed variants of CARE by leveraging the traditional saliency
methods or a pretrained segmentation model for bounding box generation. Results
show that the CARE variants with automated bounding box generation are
comparable to the original CARE framework with \textit{manual} bounding box
annotations. A series of experiments on an imbalanced skin image dataset and a
pneumonia dataset indicates that our method can effectively help the network
focus on the lesion regions of rare diseases and remarkably improves the
classification performance of rare diseases.

提出了一种简单而有效的框架，名为 CARE（Class Attention to Regions of the Lesion），通过在卷积神经网络（CNNs）的训练过程中引入注意力机制来处理数据不平衡问题。实验证明，该方法能够有效帮助网络专注于罕见疾病的损伤区域，显著提高了罕见疾病的分类性能。

不平衡医学图像识别中的病变区域类别注意力

Class Attention to Regions of Lesion for Imbalanced Medical Image  Recognition

Automated medical image classification with convolutional neural networks
(CNNs) has great potential to impact healthcare, particularly in
resource-constrained healthcare systems where fewer trained radiologists are
available. However, little is known about how well a trained CNN can perform on
images with the increased noise levels, different acquisition protocols, or
additional artifacts that may arise when using low-cost scanners, which can be
underrepresented in datasets collected from well-funded hospitals. In this
work, we investigate how a model trained to triage head computed tomography
(CT) scans performs on images acquired with reduced x-ray tube current, fewer
projections per gantry rotation, and limited angle scans. These changes can
reduce the cost of the scanner and demands on electrical power but come at the
expense of increased image noise and artifacts. We first develop a model to
triage head CTs and report an area under the receiver operating characteristic
curve (AUROC) of 0.77. We then show that the trained model is robust to reduced
tube current and fewer projections, with the AUROC dropping only 0.65% for
images acquired with a 16x reduction in tube current and 0.22% for images
acquired with 8x fewer projections. Finally, for significantly degraded images
acquired by a limited angle scan, we show that a model trained specifically to
classify such images can overcome the technological limitations to
reconstruction and maintain an AUROC within 0.09% of the original model.

通过使用卷积神经网络自动分类医学图像，我们研究了在使用低成本扫描仪时，训练 CNN 模型的性能如何。我们发现，我们的模型对于更低的 X 射线管电流和更少的图片被拍摄的角度的图片都有很好的分类效果。然而，在使用受限扫描器获得的显着劣化的图像方面，我们发现训练特定于这种图像的模型可以克服技术限制并保持接近于原始模型的性能。