Improving CXR Bone Suppression by Exploiting Domain-level and Instance-level Information.

For chest X-ray image (CXR) analysis, effective bone structure suppression is essential for uncovering lung abnormalities and facilitating accurate clinical diagnoses. While recent deep generative models, to some extent, improve the reconstruction quality of bone-suppressed CXRs, they often fall short in delivering substantial improvements in downstream diagnosis tasks. This limitation is attributed to a narrow focus on instance-specific details, neglecting broader domain-level knowledge, which hampers bone-suppression effectiveness. In response to these challenges, our proposed framework adopts a novel approach that integrates both instance-level and domain-level information. To capture instance information, our model employs a hybrid approach using both cross-covariance attention blocks (CABs) to underscore relevant image information and a followed Vision Transformers (ViTs) encoder for image feature embedding. To capture domain information, we introduce multi-head codebook attention (MCA) which leverages codebook structure with multi-head attention mechanism to capture global, domain-level information specific to the bone-suppressed CXR domain, thereby refining the synthesis process. During optimization, our two-stage training scheme involves a MCA learning stage that encapsulates the domain of bone-suppressed CXRs in MCA through a ViT-based GAN model, and a synthesis stage that employs the learned codebook to generate bone-suppressed CXRs from the original ones, enhancing instance synthesis through domain insights. Moreover, the incorporation of CABs further refines pixellevel instance information. Extensive experiments demonstrate the superior performance of our approach, improving PSNR by 8.36% and SSIM by 2.7% for bone suppression while boosting lung disease classification by 2.8% and 4.2% on two datasets and segmentation by 1.5%.