Facial expression recognition has wide application prospects in many occasions. Due to the complexity and variability of facial expressions, facial expression recognition has become a very challenging research topic. This paper proposes a Vision Transformer expression recognition method based on hybrid local attention (HLA-ViT). The network adopts a dual-stream structure. One stream extracts the hybrid local features and the other stream extracts the global contextual features. These two streams constitute a global–local fusion attention. The hybrid local attention module is proposed to enhance the network’s robustness to face occlusion and head pose variations. The convolutional neural network is combined with the hybrid local attention module to obtain feature maps with local prominent information. Robust features are then captured by the ViT from the global perspective of the visual sequence context. Finally, the decision-level fusion mechanism fuses the expression features with local prominent information, adding complementary information to enhance the network’s recognition performance and robustness against interference factors such as occlusion and head posture changes in natural scenes. Extensive experiments demonstrate that our HLA-ViT network achieves an excellent performance with 90.45% on RAF-DB, 90.13% on FERPlus, and 65.07% on AffectNet.

Purpose Mammographic density (MD) is a strong risk factor for breast cancer, yet its relationship with tumor characteristics is not well established, particularly in Asian populations. Methods MD was assessed from a total of 2001 Chinese breast cancer patients using Breast Imaging Reporting and Data System (BI-RADS) categories. Molecular subtypes were defined using immunohistochemical status on ER, PR, HER2, and Ki-67, as well as tumor grade. Multinomial logistic regression was used to test associations between MD and molecular subtype (luminal A = reference) adjusting for age, body mass index (BMI), menopausal status, parity, and nodal status. Results The mean age at diagnosis was 51.7 years (SD = 10.7) and the average BMI was 24.7 kg/m² (SD = 3.8). The distribution of BI-RADS categories was 7.4% A = almost entirely fat, 24.2% B = scattered fibroglandular dense, 49.4% C = heterogeneously dense, and 19.0% D = extremely dense. Compared to women with BI-RADS = A/B, women with BI-RADS = D were more likely to have HER2-enriched tumors (OR = 1.81, 95% CI 1.08–3.06, p = 0.03), regardless of menopausal status. The association was only observed in women with normal (< 25 kg/m²) BMI (OR = 2.43, 95% CI 1.24–4.76, p < 0.01), but not among overweight/obese women (OR: 0.98, 95% CI 0.38–2.52, p = 0.96). Conclusions Among Chinese women with normal BMI, higher breast density was associated with HER2-enriched tumors. The results may partially explain the higher proportion of HER2+ tumors previously reported in Asian women.

Facial expressions help individuals convey their emotions. In recent years, thanks to the development of computer vision technology, facial expression recognition (FER) has become a research hotspot and made remarkable progress. However, human faces in real-world environments are affected by various unfavorable factors, such as facial occlusion and head pose changes, which are seldom encountered in controlled laboratory settings. These factors often lead to a reduction in expression recognition accuracy. Inspired by the recent success of transformers in many computer vision tasks, we propose a model called the fine-tuned channel–spatial attention transformer (FT-CSAT) to improve the accuracy of recognition of FER in the wild. FT-CSAT consists of two crucial components: channel–spatial attention module and fine-tuning module. In the channel–spatial attention module, the feature map is input into the channel attention module and the spatial attention module sequentially. The final output feature map will effectively incorporate both channel information and spatial information. Consequently, the network becomes adept at focusing on relevant and meaningful features associated with facial expressions. To further improve the model’s performance while controlling the number of excessive parameters, we employ a fine-tuning method. Extensive experimental results demonstrate that our FT-CSAT outperforms the state-of-the-art methods on two benchmark datasets: RAF-DB and FERPlus. The achieved recognition accuracy is 88.61% and 89.26%, respectively. Furthermore, to evaluate the robustness of FT-CSAT in the case of facial occlusion and head pose changes, we take tests on Occlusion-RAF-DB and Pose-RAF-DB data sets, and the results also show that the superior recognition performance of the proposed method under such conditions.

In recent years, significant progress has been made in facial expression recognition methods. However, tasks related to facial expression recognition in real environments still require further research. This paper proposes a tri-cross-attention transformer with a multi-feature fusion network (TriCAFFNet) to improve facial expression recognition performance under challenging conditions. By combining LBP (Local Binary Pattern) features, HOG (Histogram of Oriented Gradients) features, landmark features, and CNN (convolutional neural network) features from facial images, the model is provided with a rich input to improve its ability to discern subtle differences between images. Additionally, tri-cross-attention blocks are designed to facilitate information exchange between different features, enabling mutual guidance among different features to capture salient attention. Extensive experiments on several widely used datasets show that our TriCAFFNet achieves the SOTA performance on RAF-DB with 92.17%, AffectNet (7 cls) with 67.40%, and AffectNet (8 cls) with 63.49%, respectively.