Weakly supervised learning based on image-level labels can effectively reduce annotation costs, making it a popular choice for histopathological tissue segmentation. However, this pattern still face some challenges: 1) inaccurate class activation maps (CAM) make pseudo masks quality insufficient; 2) noisy pixels in pseudo masks will mislead the segmentation model's decision-making. To deal with these problems, we propose a novel weakly supervised semantic segmentation (WSSS) framework. First, we introduce Local Spatial Affine Perturbation to strengthen the model's utilization of weak supervision signals and improve its robustness to noisy regions within CAM. Second, we propose Wave-aware Dynamic Feature Aggregation to adaptively enhance the information-aware representation of target regions to obtain fine-grained pseudo masks enriched with positive semantic information. Third, we train a segmentation model with a noise-suppression scheme called Cross-scale Logits Distillation to reduce the inevitable false positive pixels in pseudo masks. We conduct extensive experiments to validate our method and set new state-of-the-art segmentation performances on five histopathological tissue segmentation datasets. Moreover, we will introduce a new dataset, GCSS-WSSS for gastric cancer, to promote the diversification for the research community of computational pathology. Code and data will be released at: https://github.com/director87/WaWeHis.

Cancer is a leading cause of death worldwide due to its aggressive nature and complex variability. Accurate prognosis is therefore challenging but essential for guiding personalized treatment and follow-up. Previous research often relied on single data sources, missing the opportunity to combine various types of patient information for more comprehensive survival predictions. To address these challenges, we propose a two-stage fusion method named Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework (CA-MLIF). In the first stage, we propose a CA mechanism for real-time feature updates and cross-modal mutual learning to capture rich semantic information. In the second stage, we design a novel multimodal low-rank interaction fusion method for survival prediction. Specifically, we present modal attention mechanism (MAM) for feature filtration, low-rank multimodal fusion (LMF) for model complexity reduction, and optimal weight concatenation (OWC) for maximizing feature integration. Extensive experiments on two public datasets TCGA-GBMLGG and TCGA-KIRC, as well as a multi-center in-house lung adenocarcinoma (LUAD) dataset validate the effectiveness of CA-MLIF, which demonstrate that our method outperforms existing approaches in survival prediction under both pathology-gene fusion and CT-pathology fusion scenarios.

Image-level weakly supervised semantic segmentation (WSSS) reduces the dependence on high-quality data annotation, which plays a crucial role in computational pathology. Benefit from the ability to localize the objects with only binary labels, Class Activation Map (CAM) is a widely used method to initial pseudo masks. However, due to the low contrast among different tissues in histopathological images, most existing CAM-based methods perform poorly in gland segmentation. We retrospect this process and find that class consistency and semantic consistency can guide the network to effectively distinguish confusing pixels and generate fine-grained pseudo masks. Specifically, for class consistency, we propose Consistency Correlation Attention (CCA) to encourage the network to focus on the contribution of class features to semantic dependencies. For semantic consistency, we propose Multi-scale Pyramid Fusion Pooling (MPFP) to aggregate coarse-to-fine global semantic information from CAMs at multiple spatial resolutions, thus identifying class localization. Additionally, we introduce a Purified Labels Filtration (PLF) strategy during the segmentation phase to mitigate the noisy supervision signal and improve the segmentation quality of the model. Extensive experiments show that the our method achieves new state-of-the-art results on three publicly available gland datasets. Furthermore, our method demonstrates impressive domain adaptation capability, achieving satisfactory results with only a small portion of samples when faced with unseen domain data.

Social robots (SRs) are interactive systems designed to engage in human-centered communication through both verbal and nonverbal behaviors. To enable emotionally responsive interaction, SRs require affective feedback systems capable of recognizing user emotions and generating appropriate expressive responses. This study investigates the design of affective feedback systems in SRs. Guided by the FCBPSS framework, the system is analyzed through two core components. The first is the use of artificial intelligence techniques to process physiological and behavioral signals for emotion recognition. The second involves the generation of affective responses through multimodal output channels such as facial expressions, speech, haptics, and lighting. Based on this analysis, this study proposes an affective feedback framework that integrates AI-based emotion recognition with multimodal expressive interaction to support dynamic, human-centered robot behavior. The framework provides a structured foundation for analyzing and developing affective interaction systems in SRs, contributing to emotionally intelligent interaction models that are adaptive, socially aware, and grounded in systematic design principles.