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Advanced Intelligent Computing Technology and Applications 21st International Conference, ICIC 2025, Ningbo, China, July 26–29, 2025, Proceedings, Part XXVIII /

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 pr...

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Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Huang, De-Shuang (Editor), Chen, Wei (Editor), Pan, Yijie (Editor), Chen, Haiming (Editor)
Formato: Electrónico eBook
Lenguaje:inglés
Publicado: Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
Edición:1st ed. 2025.
Colección:Lecture Notes in Bioinformatics, 15869
Materias:
Computational intelligence.
Computer networks .
Machine learning.
Application software.
Computational Intelligence.
Computer Communication Networks.
Machine Learning.
Computer and Information Systems Applications.
Acceso en línea:https://doi.org/10.1007/978-981-95-0036-9
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Tabla de Contenidos:
  • -- 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 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.

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