{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":23,"n_cols":5,"rows_kept":23,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[16,2,16,3],[17,2,17,3],[19,2,19,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996668465203316"]
[null,null,"Titolo",null,"Advanced Intelligent Computing Technology and Applications : 21st International Conference, ICIC 2025, Ningbo, China, July 26–29, 2025, Proceedings, Part XXVIII / / edited by De-Shuang Huang, Wei Chen, Yijie Pan, Haiming Chen"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"981-9500-36-2"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XXI, 511 p. 181 illus., 169 illus. in color.)"]
[null,null,"Collana",null,"Lecture Notes in Bioinformatics, , 2366-6331 ; ; 15869"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-- Machine Learning.  -- Identifying spatial domains by fusing spatial transcriptomics and histological images through contrastive learning.  -- A Medical Image Segmentation Network Based on Adaptive Feature Attention and Multi-scale Feature Extraction.  -- A Preliminary Exploration of Children Autism Spectrum Disorder Detection Based on Environmental Variables.  -- A Novel Approach for Drug-Drug Interaction Prediction: Utilizing Enhanced Graph Convolutional Networks and 3D Chemical Structures.  -- BMC-Net: A Framework for IDH Genotyping of Gliomas Based on Bi directional Mamba Sequences.  -- Integrating Radiomics and Deep Learning for Enhanced Three-Dimensional Meningioma Grading.  -- SeqAlignXGBoost: Sequence Alignment and Feature Selection for m1A Modification Site Identification.  -- Leveraging Large Language Models for Early Diagnosis of Inherited Metabolic Diseases Evaluation and Optimization.  -- HAP-MT: Alternating Perturbation Strategies Across Data and Feature Levels in semi-supervised medical image segmentation.  -- MTSN: A Multi-granularity Temporal Sleep Network for Sleep Apnea "]
[{"$nulls":4},"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 Framework for Few-Shot Medical Image Classification via Multi Agent Description Generation and Refined Contrastive Learning.  -- WCG-Net: A Multi-Frequency Perception Network for Medical Image Segmentation.  -- TransEdge: Leveraging Transformer and EfficientKAN with Edge Sensitivity for Advanced Medical Image Segmentation."]
[null,null,null,"Sommario/riassunto","The 20-volume set LNCS 15842-15861, together with the 4-volume set LNAI 15862-15865 and the 4-volume set LNBI 15866-15869, constitutes the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The 1206 papers presented in these proceedings books were carefully reviewed and selected from 4032 submissions. They deal with emerging and challenging topics in artificial intelligence, machine learning, pattern recognition, "]
[{"$nulls":4},"bioinformatics, and computational biology. ."]