In clinical practice, panoramic dental radiography is a widely employed imaging technique that can provide a detailed and comprehensive view of dental structures and surrounding tissues for identifying various oral anomalies. However, due to the complexity of oral anomalies and the scarcity of available data, existing research still suffers from substantial challenges in automated oral anomaly detection. To this end, this paper presents a new hospital-scale panoramic X-ray benchmark, namely "OralXrays-91", which consists of 12,688 panoramic X-ray images with 84,113 meticulously annotated instances across nine common oral anomalies. Correspondingly, we propose a personalized Multi-Object Query-Aware Mining (MOQAM) paradigm, which jointly incorporates the Distribution-IoU Region Proposal Network (DI-RPN) and Class-Balanced Spherical Contrastive Regularization (CB-SCR) mechanisms to address the challenges posed by multi-scale variations and class-imbalanced distributions. To the best of our knowledge, this is the first attempt to develop AI-driven diagnostic systems specifically designed for multi-object oral anomaly detection, utilizing publicly available data resources. Extensive experiments on the newly-published OralXrays-9 dataset and real-world nature scenarios consistently demonstrate the superiority of our MOQAM in revolutionizing oral healthcare practices.

Few-shot Class Incremental Learning (FSCIL) aims to incrementally learn new classes with limited examples while retaining knowledge of previously learned classes. Recent advancements in prompt tuning for large pre-trained models have shown promise in FSCIL. However, current FSCIL methods still suffer from challenges like insufficient plasticity and limited generalization. To tackle these challenges, we propose a novel prompt tuning-based framework named A2GP-SF, which integrates attribute generative prompting (AGP) and adaptive sharpness flattening (ASF). The proposed AGP paradigm dynamically generates attribute-aware prompts for each instance, facilitating better semantics learning and enhancing plasticity. Additionally, the ASF mechanism aims to mitigate overfitting by applying adaptive perturbations to flatten sharpness, with these perturbations adjusted based on gradient norm changes, thereby enhancing the model’s robustness and generalization. Extensive experiments on multiple benchmark datasets consistently demonstrate the superiority of our proposed A2GP-SF framework.

Dental caries has been widely recognized as one of the most prevalent chronic diseases in the field of public health. Despite advancements in automated diagnosis across various medical domains, it remains a substantial challenge for dental caries detection due to its inherent variability and intricacies. To bridge this gap, we release a hospital-scale panoramic dental X-ray benchmark, namely “CariesXrays”, to facilitate the advancements in high-precision computer-aided diagnosis for dental caries. It comprises 6,000 panoramic dental X-ray images, with a total of 13,783 instances of dental caries, all meticulously annotated by dental professionals. In this paper, we propose a novel Feature Pyramid Contrastive Learning (FPCL) framework, that jointly incorporates feature pyramid learning and contrastive learning within a unified diagnostic paradigm for automated dental caries detection. Specifically, a robust dual-directional feature pyramid network (D2D-FPN) is designed to adaptively capture rich and informative contextual information from multi-level feature maps, thus enhancing the generalization ability of caries detection across different scales. Furthermore, our model is augmented with an effective proposals-prototype contrastive regularization learning (P2P-CRL) mechanism, which can flexibly bridge the semantic gaps among diverse dental caries with varying appearances, resulting in high-quality dental caries proposals. Extensive experiments on our newly-established CariesXrays benchmark demonstrate the potential of FPCL to make a significant social impact on caries diagnosis.