Gu Hong, Wang Hongyu, Qin Pan, Wang Jia
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
Department of Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
Front Med (Lausanne). 2022 Jun 2;9:923456. doi: 10.3389/fmed.2022.923456. eCollection 2022.
We consider the problem of weakly supervised segmentation on chest radiographs. The chest radiograph is the most common means of screening and diagnosing thoracic diseases. Weakly supervised deep learning models have gained increasing popularity in medical image segmentation. However, these models are not suitable for the critical characteristics presented in chest radiographs: the global symmetry of chest radiographs and dependencies between lesions and their positions. These models extract global features from the whole image to make the image-level decision. The global symmetry can lead these models to misclassification of symmetrical positions of the lesions. Thoracic diseases often have special disease prone areas in chest radiographs. There is a relationship between the lesions and their positions. In this study, we propose a weakly supervised model, called Chest L-Transformer, to take these characteristics into account. Chest L-Transformer classifies an image based on local features to avoid the misclassification caused by the global symmetry. Moreover, associated with Transformer attention mechanism, Chest L-Transformer models the dependencies between the lesions and their positions and pays more attention to the disease prone areas. Chest L-Transformer is only trained with image-level annotations for lesion segmentation. Thus, Log-Sum-Exp voting and its variant are proposed to unify the pixel-level prediction with the image-level prediction. We demonstrate a significant segmentation performance improvement over the current state-of-the-art while achieving competitive classification performance.
我们考虑胸部X光片上的弱监督分割问题。胸部X光片是筛查和诊断胸部疾病最常用的手段。弱监督深度学习模型在医学图像分割中越来越受欢迎。然而，这些模型并不适用于胸部X光片呈现出的关键特征：胸部X光片的全局对称性以及病变与其位置之间的相关性。这些模型从整个图像中提取全局特征以做出图像级别的决策。全局对称性可能导致这些模型对病变的对称位置进行错误分类。胸部疾病在胸部X光片中通常有特定的疾病高发区域。病变与其位置之间存在关联。在本研究中，我们提出了一种名为胸部L-Transformer的弱监督模型，以考虑这些特征。胸部L-Transformer基于局部特征对图像进行分类，以避免因全局对称性导致的错误分类。此外，与Transformer注意力机制相关联，胸部L-Transformer对病变与其位置之间的相关性进行建模，并更加关注疾病高发区域。胸部L-Transformer仅使用用于病变分割的图像级注释进行训练。因此，提出了对数求和指数投票及其变体，以将像素级预测与图像级预测统一起来。我们展示了相较于当前最先进技术在分割性能上的显著提升，同时实现了具有竞争力的分类性能。
