Accurate recognition of early-stage pneumoconiosis presents significant challenges due to the irregular morphology, diffuse distribution, and small size of pulmonary lesions. Existing 2D methods struggle to focus on lesion-level 3D characteristics and inter-slice correlations in localized weak lesion regions, resulting in incomplete feature extraction and inaccurate calculation of lesion volume. To obtain complete 3D fine-grained lesion features in the entire lung, this paper proposes an early pneumoconiosis recognition network (EPRNet) to enhance fine-grained feature acquisition abilities and discover inter-slice correlations, thereby improving early pneumoconiosis recognition accuracy in a more structured and flexible manner. Specifically, to obtain the fine-grained 3D features of early pneumoconiosis more comprehensively, a distance-similarity graph encoding module is proposed to construct and encode the relationships of the distributed tiny lesions within CT slices, integrating the spatial positions and the corresponding feature similarities of the lesions to improve the accuracy of pneumoconiosis feature representation. To adaptively preserve accurate graph representations of the correlations of the lesions across inter-CT slices, a hierarchical dynamic-scored adaptive pooling module is proposed to discover the potential long distance correlations between cross-slices, obtaining spatial semantic information of diffused lesions in the entire lung. Experimental results based on multiple datasets demonstrate that EPRNet achieves state-of-the-art performance while exhibiting better generalization. The ablation experiments also prove the effectiveness of each module in EPRNet.

Images are often obstructed by various obstacles due to capture limitations, hindering the observation of objects of interest. Most existing methods address occlusions from specific elements like fences or raindrops, but are constrained by the wide range of real-world obstructions, making comprehensive data collection impractical. To overcome these challenges, we propose Instruct2See, a novel zero-shot framework capable of handling both seen and unseen obstacles. The core idea of our approach is to unify obstruction removal by treating it as a soft-hard mask restoration problem, where any obstruction can be represented using multi-modal prompts, such as visual semantics and textual instructions, processed through a cross-attention unit to enhance contextual understanding and improve mode control. Additionally, a tunable mask adapter allows for dynamic soft masking, enabling real-time adjustment of inaccurate masks. Extensive experiments on both in-distribution and out-of-distribution obstacles show that Instruct2See consistently achieves strong performance and generalization in obstruction removal, regardless of whether the obstacles were present during the training phase. Code and dataset are available at https://jhscut.github.io/Instruct2See.