As industrial manufacturing scales, automating fine-grained product image analysis has become critical for quality control. However, existing approaches are hindered by limited dataset coverage and poor model generalization across diverse and complex anomaly patterns. To address these challenges, we introduce MAU-Set, a comprehensive dataset for Multi-type industrial Anomaly Understanding. It spans multiple industrial domains and features a hierarchical task structure, ranging from binary classification to complex reasoning. Alongside this dataset, we establish a rigorous evaluation protocol to facilitate fair and comprehensive model assessment. Building upon this foundation, we further present MAU-GPT, a domain-adapted multimodal large model specifically designed for industrial anomaly understanding. It incorporates a novel AMoE-LoRA mechanism that unifies anomaly-aware and generalist experts adaptation, enhancing both understanding and reasoning across diverse defect classes. Extensive experiments show that MAU-GPT consistently outperforms prior state-of-the-art methods across all domains, demonstrating strong potential for scalable and automated industrial inspection.

Cerebrovascular segmentation is essential for diagnosing and treating cerebrovascular diseases. However, accurately segmenting cerebral vessels in TOF-MRA remains challenging due to significant interindividual variations in cerebrovascular morphology, low image con-trast, and class imbalance. The present study proposes an advanced deep learning model called PPA Net, consisting of VesselMRA Net and VesselConvLSTM components. Firstly, VesselMRA Net utilizes rectangular convolutional blocks to fuse multi-scale features, enhancing feature extraction per-formance. VesselMRA Net employs the attention mechanism to boost certain valuable semantic weighting, addressing segmentation challenges arising from class imbalance and low contrast. Secondly, VesselConvLSTM, a pixel-level prediction model, employs a gating mechanism to learn cerebral vessel morphology across individuals. It reduces individual differences in segmentation and restores inter-voxel correlations disrupted by data slicing, aiding VesselMRA Net in accurately segmenting cerebrovascular pixels. Lastly, integrating VesselMRA Net and VesselConv-LSTM results in a modular cerebral vessel segmentation framework, PPA Net, facilitating separate optimization of the backbone network and predicted model components. The performance of this model has been extensively validated through experimental evaluations on three publicly available datasets, obtaining significant competitiveness when compared to the state-of-the-art of the current cerebral vessel segmentation models.