Feature sparse problem is commonly existing in patient similarity calculation task with clinical data, to track which, some approaches have been proposed to use Graph Neural Network (GNN) to model the complex structural information in patient Electronic Medical Records (EMRs). These GNN based approaches usually treat medical concepts (i.e., symptoms, diseases) as nodes to learn spatial features and adopt Recurrent Neural Network (RNN) to learn temporal sequence of these concepts. However, in many cases, several sequential concepts contained in EMR text are considered as occur simultaneously in the clinical diagnosis (i.e., some symptoms are detected simultaneously by once test), learning temporal sequence of these sequential concepts might cause noise for patient similarity calculation. Furthermore, the limited discriminative capability of concepts cannot provide sufficient indicative information for similarity learning. To this end, we propose a Knowledge-guided Event-relation Graph Learning Network (KEGLN) for patient similarity calculation. Specifically, after event extraction, we first construct element-relation graphs and use the first Graph Convolutional Network (GCN) and Graph Attention Network (GAT) layer to aggregate features from each event and its involved elements for reducing the noise produced by temporal sequence of concepts. Meanwhile, the entity description and attribute-value structure are extracted to supplement background knowledge of elements (concepts and trigger words). For the updated event nodes, we then design a event-relation graph and adopt the second GCN and GAT layer to aggregate information from events and their directly neighbors to extract spatial features of events at the current moment. Finally, the Bidirectional Long Short-Term Memory (BiLSTM) model is adopted to learn temporal dependency of event nodes to capture the dynamic change of disease progress. Through diverse datasets and extensive experiments, our KEGLN model outperforms all baselines for Chinese patient similarity calculation.

Medical image analysis frequently encounters data scarcity challenges. Transfer learning has been effective in addressing this issue while conserving computational resources. The recent advent of foundational models like the DINOv2, which uses the vision transformer architecture, has opened new opportunities in the field and gathered significant interest. However, DINOv2's performance on clinical data still needs to be verified. In this paper, we performed a glioma grading task using three clinical modalities of brain MRI data. We compared the performance of various pre-trained deep learning models, including those based on ImageNet and DINOv2, in a transfer learning context. Our focus was on understanding the impact of the freezing mechanism on performance. We also validated our findings on three other types of public datasets: chest radiography, fundus radiography, and dermoscopy. Our findings indicate that in our clinical dataset, DINOv2's performance was not as strong as ImageNet-based pre-trained models, whereas in public datasets, DINOv2 generally outperformed other models, especially when using the frozen mechanism. Similar performance was observed with various sizes of DINOv2 models across different tasks. In summary, DINOv2 is viable for medical image classification tasks, particularly with data resembling natural images. However, its effectiveness may vary with data that significantly differs from natural images such as MRI. In addition, employing smaller versions of the model can be adequate for medical task, offering resource-saving benefits. Our codes are available at https://github.com/GuanghuiFU/medical_dino_eval.

Multi-class segmentation of eye images plays a pivotal role in assessing patients with myasthenia gravis, and the measurement results rely heavily on the segmentation accuracy. However, there is still a problem with inaccurate boundary segmentation. Compared to heuristic-based network structure optimization, exploring effective loss function is an intuitive, simple, and interpretable way to address this issue. In this paper, we experimentally verify the effectiveness of boundary loss for multi-class segmentation of eye images and investigate its hybrid law with other segmentation losses. The application of the study significantly enhances the accuracy of myasthenia gravis scoring and holds promise for assisting in the evaluation of various other eye diseases.

Sign language video translation, which converts sign language information into textual expressions, play a vital role in breaking down the language communication barrier between deaf and healthy people. Existing translation methods are mainly focus on the single and pure background. However, the background in real-world environments is always complex, and these methods are difficult to achieve effective recognition results. To address this issue, we have exploratively constructed a real-world complex background sign language dataset (CBSL), containing sign language videos captured in various authentic environments (e.g., different backgrounds and lighting conditions). Based on this, we propose a progressive sign language translation model to effectively separate sign language users from the background and reduce environmental interference, thus significantly improving the generalization ability. Our proposed method significantly outperforms various comparative methods across all performance metrics on the CBSL dataset. Furthermore, on the publicly available Chinese Sign Language Continuous Recognition dataset(CSL), our method performs comparably to the current state-of-the-art (SOTA).

Myasthenia gravis (MG) is a neurological disease that is difficult to diagnose and requires long-term management. The progression of this disease is reflected to some extent in changes in speech, such as hoarseness and articulation disorders. However, it is difficult for general neurologists to grasp the diagnostic patterns of such rare diseases, especially in underdeveloped regions. As an emerging field, speech-based intelligent diagnostic assistance provides a safe, non-invasive, and convenient solution for healthcare education management. To this end, we firstly constructed a novel Chinese speech dataset of myasthenia gravis patients (MGCS). Then we proposed a network named Myasthenia Gravis Speech Net (MGS-Net) for the classification of myasthenia gravis pathological speech, which is mainly composed of two blocks: the Local Feature Enhancement (LFE) block and the Feedforward Dense (FFD) block. The LFE block extracts temporal local features using a sliding window approach, while the FFD block captures the global representation of the data. Compared to existing methods, our pipeline achieves an accuracy of 98.75% and a recall rate of 99.17%. We validated the effectiveness of existing acoustic feature sets in pathological speech classification of MG, which will provide an important tool for health education management of neurological diseases.

Sign language recognition and translation (SLRT) aims to convert sign language into textual representation, which holds significant importance for the deaf community. Sign language possesses complex and diverse grammatical structures, with each sign language having distinct motion trajectories and gesture variations, making SLRT a complex research domain. In recent years, numerous researchers have proposed differ-ent modeling approaches, achieving significant advancements through the utilization of large language models. In this survey, we systematically review the developmental trajectory of SLRT, encompassing an introduction to key technical approaches at each stage and the latest research progress. Through a comprehensive examination of these methods, valuable insights are provided for future research and practical applications. Lastly, we identify the existing limitations of current methods and propose potential avenues for future research.