BACKGROUND: Organoid image segmentation is essential for quantitative analysis in disease modeling and drug screening, yet remains highly challenging due to substantial morphological variability and blurred boundaries in organoid images. Existing approaches often struggle to achieve a favorable balance between segmentation accuracy and computational efficiency. RESULTS: In this paper, DeShiftNet, a lightweight segmentation framework, is proposed to extract discriminative features with high accuracy while maintaining low computational overhead. The model incorporates a deformable-shifted encoding strategy that adaptively samples local structures. It also includes a cross-attention-guided decoder for selective multi-scale feature alignment. Furthermore, a deformable multi-scale contextual refinement module enhances boundary coherence and contextual consistency. Extensive experiments on the multi-type OrganoID dataset show that DeShiftNet achieves competitive performance compared with recent segmentation models, while maintaining only 1.78M parameters and 2.65 GFLOPs. Notably, DeShiftNet achieves a Dice score of 0.961 on the Lung subset. CONCLUSION: These results indicate its potential practical value for efficient organoid segmentation in high-throughput experimental workflows.

Accurate and automated identification of organoids from bright-field images is essential for enabling high-throughput drug screening and precision medicine. Organoids, as 3-D in vitro cellular models, closely recapitulate the functional and structural characteristics of their tissue or organ of origin, presenting an unprecedented opportunity for biomedical research. However, the complexity of bright-field microscopy images, including heterogeneous backgrounds and diverse organoid morphologies, poses significant challenges for existing computational methods, often hindering robust feature extraction and high-throughput analysis. To address these issues at the intersection of computational vision and organoid biology, we propose FEMSSorg, a novel organoid recognition framework designed to adaptively aggregate multiscale scan-selected state space features through a fuzzy mixture-of-experts (FuzzyMoE) scoring mechanism. FEMSSorg introduces a fuzzy expert soft routing mechanism (fuzzy route), implemented via Fuzzy C-Means-based soft routing assignments, forming a new class of fuzzy MoE that leverages fuzzy expert clustering scores to dynamically integrate local (LocalSS) and global (GlobalSS) state space features. This approach enables effective balancing of global pixel dependencies and local texture information, thereby substantially reducing background interference and image noise in bright-field images and improving the accuracy of organoid identification. Furthermore, we incorporate a Dual Downsampling Adaptive Pooling Feature Fusion module, which combines original backbone features with parallel downsampled features and utilizes content-aware pooling for adaptive multilevel and multiscale feature fusion. Experimental results on multiclass organoid bright-field image datasets demonstrate that FEMSSorg achieves state-of-the-art performance in both organoid detection and morphological texture classification, highlighting its value as a robust computational tool for advancing real-time, high-throughput organoid research.

In the field of artificial intelligence for information technology operations, operational data are often modeled as aperiodic multivariate time series, which contain rich multidimensional and nonlinear patterns. However, the existing approaches are unable to effectively acquire knowledge and recognize patterns due to their reliance on processing and modeling periodic patterns. To address this issue, this article proposes a novel deep-saliency-aware dual embedded attention network for aperiodic multivariate time-series forecasting. Our network consists of three main components: 1) a convolutional-neural-network- and transformer-based component for saliency representation of the aperiodic patterns; 2) a lightweight recurrent neural network component for capturing long-term dependence features; and 3) an attention mechanism for fusing the latent representations from the former components. Extensive empirical studies are conducted on a real-world dataset and five other public datasets to evaluate the proposed network against four state-of-the-art models. The results show that our method achieves impressive high performance on most evaluation metrics. Furthermore, the data and code used in this study are publicly available, which can facilitate progress in the community.

The utilization of web social media for depression detection has been proven effective in recent years since the multimedia signal on web can reflect users’ emotions, feelings, and personality traits in advance. However, most earlier studies simply used users’ submitted words or user profiles to predict depression risk. The implicit information accessible in users’ posted images, which can be effective in depression detection, still remains unexplored. In this paper, an implicit and explicit multi-modal feature fusion (IEMFF) model is proposed for depression detection. We successfully make the implicit information inherent in users’ posted images explicit and further incorporate such explicit features with the textual features directly extracted from user-posted texts. A multi-modal feature fusion approach is applied for depression detection. Extensive experiments have been conducted on public Twitter datasets. Experimental results show that our approach has achieved state-of-the-art performance for depression detection.

The approaches based on deep learning have achieved remarkable success in diabetic retinopathy detection. Due to the accountability in medical diagnosis, the interpretability of computer-aided diagnosis has recently been investigated. However, few existing approaches make full use of the explainable evidence to improve the diagnosis accuracy. In this paper, we propose a Model-free Lesion Generation and Learning (MLGL) framework to study the interpretability of diabetic retinopathy detection. We first generate visual explanations for diabetic retinopathy diagnosis using the proposed Gated Multi-layer Saliency Map (GMSM) module, which locates the accurate region of lesions by combining multi-layer heatmaps. Then we use the GMSM to extract the lesion patches and conduct the adaptive lesion transfer, iteratively generating new retinal fundus images with lesions. Especially, in this process, no additional generative models are trained. Finally, we merge the generated and original retinal fundus images for the model's training to learn robust lesion features. Overall, our method provides accurate explainable evidence and further addresses the data imbalance problem in diabetic retinopathy detection. The experimental results on four public datasets demonstrate the efficiency of our approach.

Root cause analysis for multi-dimensional time series from large scale micro-service scenarios aims at identifying the set of anomaly attributes by monitoring operational metrics. The online metrics provide a general indication to investigate these attributes' inter-dependencies and can guide the overall exploration process. However, the problem space for the root cause localization still remains largely challenging due to the combinatorial explosion of possible attribute combinations. This leads researchers and practitioners to (a) assume some prior distributions on the data set; (b) assume some data patterns on the attribute combinations; (c) perform pruning techniques to reduce the search space. Furthermore, state-of-the-art root cause analysis methods are often tied to one or more of these assumptions, which makes it difficult to be robust to general scenarios. In this paper, we conclude the heterogeneity in the data patterns by analyzing several open and industrial datasets. A uniform analytical framework, PatternRCA, is proposed such that it can be aware of the patterns in the metrics while avoiding explicit assumptions about them. We design an offline learning procedure that enables the framework to detect existing data patterns, which then can guide it to do fine-grain exploration in online metrics. Our extensive evaluation results show that PatternRCA outperforms state-of-the-art models with better benchmark results in public datasets. Meanwhile, it can scale to complex root cause analysis tasks on datasets with hybrid patterns in production environments.