Unleashing Feature Learning Potential via Tree-like Hierarchical Semantics for Pneumonia Lesion Data Mining

Pneumonia lesion data mining inevitably deals with the low-resolution Chest X-Ray (CXR) images, posing a pivotal challenge to effective feature learning. Existing methods resort on complicated network structures to learn the latent features, while suffer from insufficient semantics due to the low resolution, resulting in performance limitations. To address this issue, this paper presents a Tree-like Hierarchical Semantic Enhancement Network (THSE-Net), aiming to unleash the feature learning potential, with an explicit Hierarchical Binary Tree (HBT) and corresponding loss functions. Specifically, THSE-Net innovatively employs the HBT to capture hierarchical semantics, and dynamically regulates the global and local feature maps to align with the semantics; and then, it dynamically computes the HBT semantic attentions based on the enhanced features to improve the feature learning capability. On the publicly available benchmark datasets, the proposed THSE-Net enhanced the IoU metric by 0.79%-1.46%, the DSC metric by 0.9%-1.39%, and the F1 score metric by 0.66-1.15% compared with the existing methods. The code will be released on GitHub upon acceptance.