Description

Book Synopsis

.- 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.

Advanced Intelligent Computing Technology and Applications

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    A Paperback by De-Shuang Huang

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      View other formats and editions of Advanced Intelligent Computing Technology and Applications by De-Shuang Huang

      Publisher: Springer
      Publication Date: 26/08/2025
      ISBN13: 9789819500352, 978-9819500352
      ISBN10:
      Also in:
      Artificial intelligence (AI)

      Description

      Book Synopsis

      .- 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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