Advanced Intelligent Computing Technology and Applications, Kartoniert / Broschiert

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.- Machine Learning. .- Identifying spatial domains by fusing spatial transcriptomics and histological images through contrastive learning. .- A Medical Image Segmentation Network Based on Adaptive Feature Attention and Multi-scale Feature Extraction. .- A Preliminary Exploration of Children Autism Spectrum Disorder Detection Based on Environmental Variables. .- A Novel Approach for Drug-Drug Interaction Prediction: Utilizing Enhanced Graph Convolutional Networks and 3D Chemical Structures. .- BMC-Net: A Framework for IDH Genotyping of Gliomas Based on Bi directional Mamba Sequences. .- Integrating Radiomics and Deep Learning for Enhanced Three-Dimensional Meningioma Grading. .- SeqAlignXGBoost: Sequence Alignment and Feature Selection for m1A Modification Site Identification. .- Leveraging Large Language Models for Early Diagnosis of Inherited Metabolic Diseases Evaluation and Optimization. .- HAP-MT: Alternating Perturbation Strategies Across Data and Feature Levels in semi-supervised medical image segmentation. .- MTSN: A Multi-granularity Temporal Sleep Network for Sleep Apnea Detection. .- Fre-CrossFormer: Utilizing Frequency Domain Cross Attention for Accurate Noninvasive Blood Pressure Measurement. .- A Latent Diffusion Model for Molecular Optimization. .- BAGP: A Biomedical Entity-Relation Joint Extraction Model Integrating Adversarial Training with Biaffine Attention. .- A Contrastive Learning Framework for Alzheimer's Disease Classification (CLFAD). .- Intelligent Computing in Computer Vision. .- ABANet: Adaptive Boundary Aggregation Network for Medical Image Segmentation. .- SC3L-Net: Semi-supervised Retinal Layer Segmentation via Cross-task Consistency and Contrastive Learning. .- Interactive Calibration Learning and Atrous Pyramid Spatial-Channel Attention for Semi-supervised Medical Image Segmentation. .- MVCA-UNet: A Multi-scale Visual Convolutional Attention Architecture for Skin Lesion Segmentation. .- MSFM-UNet: Multi-Scan and Frequency Domain Mamba UNet for Medical Image Segmentation. .- APG-UNet: A Lightweight and Efficient Network for Medical Image Segmentation. .- DAMF-UNet: The Dual Attention Multi-Scale Information Fusion Network for Medical Image Segmentation. .- Multi-rater Medical Image Segmentation via a Mixture-of-experts Training. .- BIRF-SDG: Band Importance Aware Random Frequency Filter Based Single-source Domain Generalization for Retinal Vessel Segmentation. .- Genap: Generalizing Across the Augmentation Gap in Medical Image Segmentation Using Single-Source Domain. .- BEA-UNet: Boundary-enhanced Dual Attention UNet for Medical Image Segmentation. .- FreqSAM2-UNet: Adapter Fine-tuning Frequency-Aware Network of SAM2 for Universal Medical Segmentation. .- LDMWSeg: Latent Diffusion Models for Weakly Supervised Medical Image Segmentation. .- FSISNet: Exploring Mamba and Transformer for Polyp Segmentation. .- Mamba Based Feature Extraction and Adaptive Multilevel Feature Fusion for 3D Tumor Segmentation from Multi-modal Medical Image. .- Diakd: A Source-Free Domain Adaptation Method for Medical Image Segmentation Based on Domain-Aware Indicator and Adaptive Knowledge Distillation. .- KD-MedSAM: Lightweight Knowledge Distillation of Segment Anything Model for Multi-modality Medical Image Segmentation. .- Uncertainty-guided Feature Learning Network for Accurate Medical Image Segmentation. .- Transformer-Based Multi-label Protein Subcellular Localization Prediction. .- Gaze-and-Machine Dual-driven Attention Fusion Network for Medical Image Classification. .- Enhanced FCM for Medical Image Segmentation Using Superpixel and Convolutional Autoencoder. .- ARB-ABD: Robust Medical Image Segmentation with Adversarial and Boundary Enhancement. .- Attentional feature fusion for pulmonary X-ray image classification. .- Co-Training with Soft-Hard Pseudo-Labels for Semi-Supervised Liver Tumor Segmentation. .- Multimodal Integration Based on Weak Alignment for Rectal Tumor Grading. .- A Unified Framewor