{"product_id":"advanced-intelligent-computing-technology-and-applications-9789819500352","title":"Advanced Intelligent Computing Technology and Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e.- Machine Learning.\u003cbr\u003e\u003c\/strong\u003e.- Identifying spatial domains by fusing spatial transcriptomics and histological images through contrastive learning.\u003cbr\u003e.- A Medical Image Segmentation Network Based on Adaptive Feature Attention and Multi-scale Feature Extraction.\u003cbr\u003e.- A Preliminary Exploration of Children Autism Spectrum Disorder Detection Based on Environmental Variables.\u003cbr\u003e.- A Novel Approach for Drug-Drug Interaction Prediction: Utilizing Enhanced Graph Convolutional Networks and 3D Chemical Structures.\u003cbr\u003e.- BMC-Net: A Framework for IDH Genotyping of Gliomas Based on Bi directional Mamba Sequences.\u003cbr\u003e.- Integrating Radiomics and Deep Learning for Enhanced Three-Dimensional Meningioma Grading.\u003cbr\u003e.- SeqAlignXGBoost: Sequence Alignment and Feature Selection for m1A Modification Site Identification.\u003cbr\u003e.- Leveraging Large Language Models for Early Diagnosis of Inherited Metabolic Diseases Evaluation and Optimization.\u003cbr\u003e.- HAP-MT: Alternating Perturbation Strategies Across Data and Feature Levels in semi-supervised medical image segmentation.\u003cbr\u003e.- MTSN: A Multi-granularity Temporal Sleep Network for Sleep Apnea Detection.\u003cbr\u003e.- Fre-CrossFormer: Utilizing Frequency Domain Cross Attention for Accurate \u003cbr\u003eNoninvasive Blood Pressure Measurement.\u003cbr\u003e.- A Latent Diffusion Model for Molecular Optimization.\u003cbr\u003e.- BAGP: A Biomedical Entity-Relation Joint Extraction Model Integrating Adversarial Training with Biaffine Attention.\u003cbr\u003e.- A Contrastive Learning Framework for Alzheimer's Disease Classification (CLFAD).\u003cbr\u003e\u003cstrong\u003e.- Intelligent Computing in Computer Vision.\u003cbr\u003e\u003c\/strong\u003e.- ABANet: Adaptive Boundary Aggregation Network for Medical Image Segmentation.\u003cbr\u003e.- SC3L-Net: Semi-supervised Retinal Layer Segmentation via Cross-task Consistency and Contrastive Learning.\u003cbr\u003e.- Interactive Calibration Learning and Atrous Pyramid Spatial-Channel Attention for Semi-supervised Medical Image Segmentation.\u003cbr\u003e.- MVCA-UNet: A Multi-scale Visual Convolutional Attention Architecture for Skin Lesion Segmentation.\u003cbr\u003e.- MSFM-UNet: Multi-Scan and Frequency Domain Mamba UNet for Medical Image Segmentation.\u003cbr\u003e.- APG-UNet: A Lightweight and Efficient Network for Medical Image Segmentation.\u003cbr\u003e.- DAMF-UNet: The Dual Attention Multi-Scale Information Fusion Network for Medical Image Segmentation.\u003cbr\u003e.- Multi-rater Medical Image Segmentation via a Mixture-of-experts Training.\u003cbr\u003e.- BIRF-SDG: Band Importance Aware Random Frequency Filter Based Single-source Domain Generalization for Retinal Vessel Segmentation.\u003cbr\u003e.- Genap: Generalizing Across the Augmentation Gap in Medical Image Segmentation Using Single-Source Domain.\u003cbr\u003e.- BEA-UNet: Boundary-enhanced Dual Attention UNet for Medical Image Segmentation.\u003cbr\u003e.- FreqSAM2-UNet: Adapter Fine-tuning Frequency-Aware Network of SAM2 for Universal Medical Segmentation.\u003cbr\u003e.- LDMWSeg: Latent Diffusion Models for Weakly Supervised Medical Image Segmentation.\u003cbr\u003e.- FSISNet: Exploring Mamba and Transformer for Polyp Segmentation.\u003cbr\u003e.- Mamba Based Feature Extraction and Adaptive Multilevel Feature Fusion for 3D Tumor Segmentation from Multi-modal Medical Image.\u003cbr\u003e.- Diakd: A Source-Free Domain Adaptation Method for Medical Image Segmentation Based on Domain-Aware Indicator and Adaptive Knowledge Distillation.\u003cbr\u003e.- KD-MedSAM: Lightweight Knowledge Distillation of Segment Anything Model for Multi-modality Medical Image Segmentation.\u003cbr\u003e.- Uncertainty-guided Feature Learning Network for Accurate Medical Image Segmentation.\u003cbr\u003e.- Transformer-Based Multi-label Protein Subcellular Localization Prediction.\u003cbr\u003e.- Gaze-and-Machine Dual-driven Attention Fusion Network for Medical Image Classification.\u003cbr\u003e.- Enhanced FCM for Medical Image Segmentation Using Superpixel and Convolutional Autoencoder.\u003cbr\u003e.- ARB-ABD: Robust Medical Image Segmentation with Adversarial and Boundary Enhancement.\u003cbr\u003e.- Attentional feature fusion for pulmonary X-ray image classification.\u003cbr\u003e.- Co-Training with Soft-Hard Pseudo-Labels for Semi-Supervised Liver Tumor Segmentation.\u003cbr\u003e.- Multimodal Integration Based on Weak Alignment for Rectal Tumor Grading.\u003cbr\u003e.- A Unified Framework for Few-Shot Medical Image Classification via Multi Agent Description Generation and Refined Contrastive Learning.\u003cbr\u003e.- WCG-Net: A Multi-Frequency Perception Network for Medical Image Segmentation.\u003cbr\u003e.- TransEdge: Leveraging Transformer and EfficientKAN with Edge Sensitivity for Advanced Medical Image Segmentation.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53212848390487,"sku":"9789819500352","price":66.49,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/advanced-intelligent-computing-technology-and-applications-9789819500352","provider":"Book Curl","version":"1.0","type":"link"}