Cushing’s syndrome is caused by excessive glucocor-ticoid secretion and often presents with facial features such as moon facies and plethora, making facial images valuable for diagnosis. Recent studies have used pre-trained CNNs for automated diagnosis using frontal facial images. However, CNNs focus on local features and may miss global facial characteristics typical of Cushing’s syndrome. Transformer-based visual models, through self-attention mechanisms, are better suited for capturing global features. The foundational model DINOv2, also based on the vision Transformer architecture, has recently attracted attention. In this study, we compared the performance of various pre-trained models, including CNNs, Transformer-based models, and foundational models like DINOv2, in a transfer learning setting. We also investigated biological sex bias and the effect of freezing mechanisms on DINOv2. Our results show that Transformer-based models, especially ViT, and DINOv2 outperformed CNNs, with ViT achieving the highest F1 score of 85.74%. All models showed higher accuracy for female samples, likely due to biological sex imbalance. Freezing mechanisms notably improved DINOv2 performance. In conclusion, Transformer-based models and DINOv2 show strong potential for classifying Cushing’s syndrome using facial images.The source code is publicly available at: https://github.com/songchangwei/Cushing-Disease-Diagnosis.

Mental health is a significant global public health issue, and psychological support hotlines play a crucial role in providing mental health assistance and identifying suicide risks at an early stage. However, the emotional expressions conveyed during these calls remain underexplored in current research. This study introduces a novel method that combines pitch acoustic features with deep learning-based features to analyze and understand emotions expressed during hotline interactions. Using data from China's largest psychological support hotline, which includes 105 subjects, our method achieved an F1-score of 79.13% for negative binary emotion classification. Additionally, the proposed approach was validated on an open dataset for multi-class emotion classification, where it demonstrated better performance compared to the state-of-the-art methods. To explore its clinical relevance, we applied the model to analysis the frequency of negative emotions and the rate of emotional change in the conversation, comparing 46 subjects with suicidal behavior to those without. While the suicidal group exhibited more frequent emotional changes than the non-suicidal group, the difference was not statistically significant. Importantly, our findings suggest that emotional fluctuation intensity and frequency could serve as novel features for psychological assessment scales and suicide risk prediction. The proposed method provides valuable insights into emotional dynamics and has the potential to advance early intervention and improve suicide prevention strategies through integration with clinical tools and assessments. The source code is publicly available at: https://github.com/Sco-field/Speechemotionrecognition/tree/main.

In recent years, Neural Architecture Search (NAS) has garnered widespread attention in the field of deep learning due to its significant potential in automating the construction of deep models. However, existing NAS methods primarily focus on optimizing network architecture, utilizing search strategies to find a high-performing network architecture within the search space as effectively as possible. And this process often requires repetitive and continuous searching and evaluation. With the significant advancements in Artificial Intelligence Generated Content (AIGC), an increasing number of researchers are utilizing deep generative models to create graph data. Neural network architecture can be viewed as Directed Acyclic Graphs(DAG) with labeled nodes. Therefore, we propose a graph generation model based on Graph Convolutional Network (GCN) and Generative Adversarial Network(GAN) to generate network architecture. With the aim of avoiding the repetitive and continuous searching and evaluation process in NAS. The CNN architecture generated by our algorithm in this paper achieves an accuracy of 94.37% on the CIFAR-10 dataset. While it may not outperform many other CNN models in terms of performance, it doesn’t require any expert knowledge and is generated automatically by the model, avoiding the need for repetitive searching and evaluation.

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.

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).

Suicide and suicidal behaviors remain significant challenges for public policy and healthcare. In response, psy-chological support hotlines have been established worldwide to provide immediate help to individuals in mental crises. The effectiveness of these hotlines largely depends on accurately identifying callers' emotional states, particularly underlying negative emotions indicative of increased suicide risk. However, the high demand for psychological interventions often results in a shortage of professional operators, highlighting the need for an effective speech emotion recognition model. This model would automatically detect and analyze callers' emotions, facil-itating integration into hotline services. Additionally, it would enable large-scale data analysis of psychological support hotline interactions to explore psychological phenomena and behaviors across populations. Our study utilizes data from the Beijing psychological support hotline, the largest suicide hotline in China. We analyzed speech data from 105 callers containing 20,630 segments and categorized them into 11 types of negative emotions. We developed a negative emotion recognition model and a fine-grained multi-label classification model using a large-scale pretrained model. Our experiments indicate that the negative emotion recognition model achieves a maximum F1-score of 76.96%. However, it shows limited efficacy in the fine-grained multi-label classification task, with the best model achieving only a 41.74% weighted F1-score. We conducted an error analysis for this task, discussed potential future improvements, and considered the clinical application possibilities of our study. All the codes are public available at: https://github.com/cz10914/psy_hotline_analysis.

Acute intracerebral hemorrhage is a life-threatening condition that demands immediate medical intervention. Intraparenchymal hemorrhage (IPH) and intraventricular hemorrhage (IVH) are critical subtypes of this condition. Clinically, when such hemorrhages are suspected, immediate CT scanning is essential to assess the extent of the bleeding and to facilitate the formulation of a targeted treatment plan. While current research in deep learning has largely focused on qualitative analyses, such as identifying subtypes of cerebral hemorrhages, there remains a significant gap in quantitative analysis crucial for enhancing clinical treatments. Addressing this gap, our paper introduces a dataset comprising 222 CT annotations, sourced from the RSNA 2019 Brain CT Hemorrhage Challenge and meticulously annotated at the voxel level for precise IPH and IVH segmentation. This dataset was utilized to train and evaluate seven advanced medical image segmentation algorithms, with the goal of refining the accuracy of segmentation for these hemorrhages. Our findings demonstrate that this dataset not only furthers the development of sophisticated segmentation algorithms but also substantially aids scientific research and clinical practice by improving the diagnosis and management of these severe hemorrhages. Our dataset and codes are available at https://github.com/songchangwei/3DCT-SD-IVH-ICH.