With the growing global threat of coronary artery disease (CAD), automated CAD diagnosis techniques based on coronary CT angiography (CCTA) have been developed. However, their clinical applicability remains limited due to the heterogeneity of stenosis and plaque attributes, as well as confounders within the causal relationships of CAD diagnosis. This work introduces the Attribute-Decoupled Intervention Network (ADI-Net), a confounder-free CAD diagnosis framework designed for fine-grained analysis at both the artery and patient levels, aligning with real-world clinical practice. ADI-Net employs an attribute-decoupled representation that effectively captures the heterogeneous features of stenosis and plaque with differential constraints, enabling precise, fine-grained classification. Additionally, the dynamic-updating causal intervention continuously refines confounder banks and applies the Do-expression within a complete causality, ensuring comprehensive, cross-level assessments. Experiments on CCTA datasets from three clinical centers demonstrate that ADI-Net outperforms state-of-the-art methods in cross-level, fine-grained CAD diagnosis, exhibiting superior robustness, domain adaptability, and data efficiency.

Coronary Artery Disease (CAD) poses a significant threat to cardiovascular patients worldwide, underscoring the critical importance of automated CAD diagnostic technologies in clinical practice. Previous technologies for lesion assessment in Coronary CT Angiography (CCTA) images have been insufficient in terms of interpretability, resulting in solutions that lack clinical reliability in both network architecture and prediction outcomes, even when diagnoses are accurate. To address the limitation of interpretability, we introduce the Trusted Lesion-Assessment Network (TLA-Net), which provides a clinically reliable solution for multi-view CAD diagnosis: (1) The causality-informed evidence collection constructs a causal graph for the diagnostic process and implements causal interventions, preventing confounders' interference and enhancing the transparency of the network architecture. (2) The clinically-aligned uncertainty integration hierarchically combines Dirichlet distributions from various views based on clinical priors, offering confidence coefficients for prediction outcomes that align with physicians' image analysis procedures. Experimental results on a dataset of 2,618 lesions demonstrate that TLA-Net, supported by its interpretable methodological design, exhibits superior performance with outstanding generalization, domain adaptability, and robustness.

The latest clinical guideline of the Coronary Artery Disease Reporting and Data System (CAD-RADS) emphasizes comprehensive CAD risk evaluation, driving the development of automated diagnosis technologies toward a unified multi-task framework. Previous task-specific architectures, relying on varied pre- and post-processing, showed redundancy and prediction inconsistencies. To address this, we first proposed synergistic multitask learning and constructed a unified CAD-RADS framework. It provides a collaborative diagnosis of the CAD-RADS level, coronary artery calcium, the segment involvement score, and abnormality modifiers based on the patient's CT Angiography (CTA) volume. On the one hand, we integrate offset features across multiple scales to learn distinct attention distributions for different tasks in the latent space, thereby meeting the representation requirements for task customization within a unified architecture. On the other hand, we employ ExpectationMaximization (EM)-driven iterative optimization to interactively learn a compact basis consensus among tasks, balancing them and promoting semantic complementarity. Experimental results based on CTA volumes from 1,068 patients demonstrate our framework outperforms state-of-the-art methods, advancing the clinical application of intelligent CAD-RADS.

Metal artifacts severely degrade the quality of CT images. Existing learning-based metal artifact reduction (MAR) methods often miss multi-scale anatomy and fail to transfer from synthetic to clinical scans. We present CLEAR-Net, which injects clinical priors and adaptively aligns corrupted features via two modules: CEAB, a quantized multi-scale anatomy bank distilled from clean clinical CTs, and FLAG, a scale-wise gating mechanism that aligns features to CEAB priors across domains. This structure-aware design preserves organ boundaries and fine textures, boosting robustness and generalization. Extensive experiments demonstrate CLEAR-Net's superior performance. The source code will be made publicly available.

In image generation, Schrödinger Bridge (SB)-based methods theoretically enhance the efficiency and quality compared to the diffusion models by finding the least costly path between two distributions. However, they are computationally expensive and time-consuming when applied to complex image data. The reason is that they focus on fitting globally optimal paths in high-dimensional spaces, directly generating images as next step on the path using complex networks through self-supervised training, which typically results in a gap with the global optimum. Meanwhile, most diffusion models are in the same path subspace generated by weights fA(t) and fB(t), as they follow the paradigm (xt= fA(t)xImg+ fB(t)ϵ). To address the limitations of SB-based methods, this paper proposes for the first time to find local Diffusion Schrödinger Bridges (LDSB) in the diffusion path subspace, which strengthens the connection between the SB problem and diffusion models. Specifically, our method optimizes the diffusion paths using Kolmogorov-Arnold Network (KAN), which has the advantage of resistance to forgetting and continuous output. The experiment shows that our LDSB significantly improves the quality and efficiency of image generation using the same pretrained denoising network and the KAN for optimising is only less than 0.1MB. The FID metric is reduced by more than 15%, especially with a reduction of 48.50% when NFE of DDIM is 5 for the CelebA dataset. Code is available at https://github.com/PerceptionComputingLab/LDSB.

Guidewire Artifact Removal (GAR) involves restoring missing imaging signals in areas of IntraVascular Optical Coherence Tomography (IVOCT) videos affected by guidewire artifacts. GAR helps overcome imaging defects and minimizes the impact of missing signals on the diagnosis of CardioVascular Diseases (CVDs). To restore the actual vascular and lesion information within the artifact area, we propose a reliable Trajectory-aware Adaptive imaging Clue analysis Network (TAC-Net) that includes two innovative designs: (i) Adaptive clue aggregation, which considers both texture-focused original (ORI) videos and structure-focused relative total variation (RTV) videos, and suppresses texture-structure imbalance with an active weight-adaptation mechanism; (ii) Trajectory-aware Transformer, which uses a novel attention calculation to perceive the attention distribution of artifact trajectories and avoid the interference of irregular and non-uniform artifacts. We provide a detailed formulation for the procedure and evaluation of the GAR task and conduct comprehensive quantitative and qualitative experiments. The experimental results demonstrate that TAC-Net reliably restores the texture and structure of guidewire artifact areas as expected by experienced physicians (e.g., SSIM: 97.23%). We also discuss the value and potential of the GAR task for clinical applications and computer-aided diagnosis of CVDs.

Automatic lipreading has attracted much research interest over the past few decades. Different from English, Chinese is a tone-based language with a large alphabet and thus the correlation between Chinese characters and lip motions is more complex. Most existing methods employed an intermediate representation (usually Pinyin), and adopted a cascaded architecture for Chinese lipreading. However, such a cascaded structure may accumulate errors, and employing Pinyin as the intermediate representation would cause the loss of visual information. Moreover, these approaches do not perform well for unseen speakers due to inter-speaker variability. In this paper, we propose a cascaded Transformer-based model with a new cross-level attention mechanism, enriching the ways of information transmission between cascading structures and reducing the accumulation of errors. Multiple intermediate representations including Chinese Pinyin and the visemes are adopted to acquire multi-perspective visual and linguistic features and to improve the generalization ability for unseen speakers. Evaluations on the public sentence-level Chinese lipreading database, i.e. CMLR, have demonstrated the advantages of the proposed method in both speaker-independent and multi-speaker scenarios over state-of-the-art approaches.