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, bioinformatics, and computational biology.

Klappentext

.- 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

Inhalt

.- 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-modalit…