The precise segmentation of the glomerular basement membrane (GBM) can aid pathologists in making accurate pathological diagnoses. However, conventional methods solely focus on segmenting GBM from the background, disregarding the interconnections between GBM and its similar surrounding tissues, which leads to imprecise boundary segmentation of GBM. To address this issue, we employed a multi-category segmentation method to model the distinctions and interconnections between GBM and its similar surrounding tissues. Our experimental results demonstrate that this method can more accurately segment GBM with blurred boundaries. Historically, scholars have primarily used convolution to build models. This approach has limitation that only local information is modeled without effectively extracting global information. To address this issue, we propose a more reasonable structure combining convolution and attention, which we call the Self-Reinforcing Attention Mechanism. Experimental results indicate that the addition of the attention mechanism can help the segmentation of GBM by yielding more continuous boundaries. Finally, we incorporate the feature maps of each layer of the model in the loss function, allowing the model to focus semantic information at varying scales while also providing control over the model’s focus by adjusting the weight. Our experimental results demonstrated that the proposed method has higher performance and better generalization ability than the state-of-the-art approaches.

Fall detection is a vital task in health monitoring, as it allows the system to trigger an alert and therefore enabling faster interventions when a person experiences a fall. Although most previous approaches rely on standard RGB video data, such detailed appearance-aware monitoring poses significant privacy concerns. Depth sensors, on the other hand, are better at preserving privacy as they merely capture the distance of objects from the sensor or camera, omitting color and texture information. In this paper, we introduce a privacy-supporting solution that makes the RGB-trained model applicable in depth domain and utilizes depth data at test time for fall detection. To achieve cross-modal fall detection, we present an unsupervised RGB to Depth (RGB2Depth) cross-modal domain adaptation approach that leverages labelled RGB data and unlabelled depth data during training. Our proposed pipeline incorporates an intermediate domain module for feature bridging, modality adversarial loss for modality discrimination, classification loss for pseudo-labeled depth data and labeled source data, triplet loss that considers both source and target domains, and a novel adaptive loss weight adjustment method for improved coordination among various losses. Our approach achieves state-of-the-art results in the unsupervised RGB2Depth domain adaptation task for fall detection. Code is available at https://github.com/1015206533/privacy_supporting_fall_detection.

Semantic segmentation is a fundamental task in computer vision. In multi-class scenarios, the abundance of categories, feature similarity across classes, class imbalance, and the complexity of feature space modeling pose significant challenges in delineating features and class boundaries.Recent multi-class segmentation methods often suffer from issues such as boundary ambiguity, loss of detail, and inadequate preservation of contextual information.To address these challenges, this paper introduces the Multi-scale Frequency Feature Fusion Network (MFN), a convolutional neural network designed to effectively integrate multi-scale information. First, the Deeplab network architecture is enhanced by refining the ASPP module. Finer-grained atrous convolutions are introduced in its channels to capture multi-scale semantic information, enabling the extraction of richer and more detailed features.The network incorporates a hybrid attention module to enhance the importance of specific spatial points and strengthen feature dependencies between channels.During the feature fusion stage, the FreqFusion module adaptively smooths high-level features to address class inconsistency and boundary misalignment effectively.And a combined loss function, integrating cross-entropy loss and the Dice coefficient, is designed to improve model training and achieve optimal segmentation performance.Extensive experiments conducted on the Cityscapes and Pascal VOC datasets demonstrate that the proposed method achieves superior performance.