Jing Xiao

Knowledge Tracing (KT) serves as a fundamental component of Intelligent Tutoring Systems (ITS), enabling these systems to monitor and understand learners' progress by modeling their knowledge state. However, many existing KT models primarily focus on fitting the sequences of learners' interactions, and often overlook the knowledge state itself. This limitation leads to reduced interpretability and insufficient instructional support from the ITS. To address this challenge, we propose AlignKT, which employs a frontend-to-backend architecture to explicitly model a stable knowledge state. In this approach, the preliminary knowledge state is aligned with an additional criterion. Specifically, we define an ideal knowledge state based on pedagogical theories as the alignment criterion, providing a foundation for interpretability. We utilize five encoders to implement this set-up, and incorporate a contrastive learning module to enhance the robustness of the alignment process. Through extensive experiments, AlignKT demonstrates superior performance, outperforming seven KT baselines on three real-world datasets. It achieves state-of-the-art results on two of these datasets and exhibits competitive performance on the third. The code of this work is available at https://github.com/SCNU203/AlignKT.

Automated radiology report generation is essential in clinical practice. However, diagnosing radiological images typically requires physicians 5-10 minutes, resulting in a waste of valuable healthcare resources. Existing studies have not fully leveraged knowledge from historical radiology reports, lacking sufficient and accurate prior information. To address this, we propose a Topic-Keyword Semantic Guidance (TKSG) framework. This framework uses BiomedCLIP to accurately retrieve historical similar cases. Supported by multimodal, TKSG accurately detects topic words (disease classifications) and keywords (common symptoms) in diagnoses. The probabilities of topic terms are aggregated into a topic vector, serving as global information to guide the entire decoding process. Additionally, a semantic-guided attention module is designed to refine local decoding with keyword content, ensuring report accuracy and relevance. Experimental results show that our model achieves excellent performance on both IU X-Ray and MIMIC-CXR datasets. The code is available at https://github.com/SCNU203/TKSG.

Accurate segmentation of liver tumors is crucial for their proper diagnosis and treatment. However, achieving high levels of precision typically depends on meticulous manual annotation, a process that is not only labor-intensive but also constrained by the scarcity of large-scale, real-world datasets. These datasets are indispensable for the training and validation of segmentation algorithms. Furthermore, the liver’s considerable variation in size, shape, and pathology type poses a challenge in collecting a sufficient number of image samples that adequately represent this diversity. To surmount the challenges of the time-consuming manual annotation process and the limitations in data acquisition, there is an urgent need for the development of an efficient and comprehensive tumor generation method. Some existing approaches, such as those utilizing Gaussian blurred ellipses to simulate tumors, fail to accurately reflect the biological complexity and pathological diversity of liver tumors. In this article, we introduce an innovative tumor synthesis method Latent Diffusion Model for Pathology (LDMP) that leverages medical imaging knowledge to more accurately replicate the intricacies of liver tumor morphology and pathology. This approach aims to enhance the quality and diversity of training data, thereby improving the performance of segmentation algorithms and ultimately contributing to more precise diagnoses and treatments. The method uses deep learning techniques, particularly diffusion models, to simulate real liver CT images and incorporates the biological properties of tumors into the synthesis process to generate realistic tumor images. The quality of the synthetic images is assessed using Principal Component Analysis (PCA) and Kullback–Leibler (KL) divergence to ensure the authenticity of the tumor’s spatial structure. Experimental results show that the proposed method can significantly improve the Dice Similarity Coefficient (DSC) of the tumor segmentation model and enable researchers to freely define the size and blur degree of the tumor, thereby creating medical images with precise annotations. In addition, we introduce a self-checking step before the output of synthetic data, which provides a new paradigm in the field of image synthesis and effectively compensates for potential errors in synthetic data. Our approach not only provides an effective solution for medical image analysis but also provides high-quality synthetic image resources for medical education and clinical practice. Codes are available at: https://github.com/jhf0721/tumor .