Xiong Liang, Liu Xin, Qin Xiaolin, Li Weiling
Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, China.
University of Chinese Academy of Sciences, Beijing, China.
Front Med (Lausanne). 2024 Oct 9;11:1440585. doi: 10.3389/fmed.2024.1440585. eCollection 2024.
Accurate pneumoconiosis staging is key to early intervention and treatment planning for pneumoconiosis patients. The staging process relies on assessing the profusion level of small opacities, which are dispersed throughout the entire lung field and manifest as fine textures. While conventional convolutional neural networks (CNNs) have achieved significant success in tasks such as image classification and object recognition, they are less effective for classifying fine-grained medical images due to the need for global, orderless feature representation. This limitation often results in inaccurate staging outcomes for pneumoconiosis. In this study, we propose a deep texture encoding scheme with a suppression strategy designed to capture the global, orderless characteristics of pneumoconiosis lesions while suppressing prominent regions such as the ribs and clavicles within the lung field. To further enhance staging accuracy, we incorporate an ordinal label distribution to capture the ordinal information among profusion levels of opacities. Additionally, we employ supervised contrastive learning to develop a more discriminative feature space for downstream classification tasks. Finally, in accordance with standard practices, we evaluate the profusion levels of opacities in each subregion of the lung, rather than relying on the entire chest X-ray image. Experimental results on the pneumoconiosis dataset demonstrate the superior performance of the proposed method confirming its effectiveness for accurate pneumoconiosis staging.
准确的尘肺病分期是尘肺病患者早期干预和治疗规划的关键。分期过程依赖于评估小阴影的密集度水平，这些小阴影散布在整个肺野中，表现为细微纹理。虽然传统卷积神经网络（CNN）在图像分类和目标识别等任务中取得了显著成功，但由于需要全局、无序的特征表示，它们在对细粒度医学图像进行分类时效果较差。这种局限性常常导致尘肺病分期结果不准确。在本研究中，我们提出了一种带有抑制策略的深度纹理编码方案，旨在捕捉尘肺病病变的全局、无序特征，同时抑制肺野内的肋骨和锁骨等突出区域。为了进一步提高分期准确性，我们纳入了有序标签分布以捕捉阴影密集度水平之间的有序信息。此外，我们采用监督对比学习为下游分类任务开发更具区分性的特征空间。最后，按照标准做法，我们评估肺每个子区域内阴影的密集度水平，而不是依赖于整个胸部X光图像。在尘肺病数据集上的实验结果证明了所提方法的卓越性能，证实了其在准确的尘肺病分期方面的有效性。
