Artificial Neural Networks and Machine Learning – ICANN 2024

33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part VIII

AvMichael Wand,Kristína Malinovská

Häftad, Engelska, 2024

Del 15023 i serien Lecture Notes in Computer Science

768 kr

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Beskrivning

and fairness in machine learning.Part II - computer vision: classification; and computer vision: 3D methods. Part III - computer vision: anomaly detection; and topics in computer vision. Part IV - brain-inspired computing;

Produktinformation

Utgivningsdatum:2024-10-01
Mått:155 x 235 x 27 mm
Vikt:750 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:463
Förlag:Springer International Publishing AG
ISBN:9783031723520

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Tillämpad datateknik inom Data och IT

Innehållsförteckning

.- Biosignal Processing in Medicine and Physiology..- A deep learning multi-omics framework to combine microbiome and metabolome profiles for disease classification..- CapsDA-Net: A Convolutional Capsule Domain Adversarial Neural Network for EEG-Based Attention Recognition..- ComplicaCode: Enhancing Disease Complication Detection in Electronic Health Records throughICD Path Generation..- Depression detection based on multilevel semantic features..- Depression Diagnosis and Analysis via Multimodal Multi-order Factor Fusion..- Identify Disease-associated MiRNA-miRNA Pairs through Deep Tensor Factorization and Semi-supervised Learning..- Interpretable EHR Disease Prediction System Based on Disease Experts and Patient SimilarityGraph (DE-PSG)..- Meteorological Data based Detection of Stroke using Machine Learning Techniques..- OFNN-UNI: Enhanced Optimized Fuzzy Neural Networks based on Unineurons for Advanced SepsisClassification..- ProTeM: Unifying Protein Function Prediction via Text Matching..- SnoreOxiNet: Non-contact Diagnosis of Nocturnal Hypoxemia Using Cross-domain Acoustic Features..- Unveiling the Potential of Synthetic Data in Sports Science: A Comparative Study of Generative Methods..- Medical Image Processing..- Adaptive Fusion Boundary-Enhanced Multilayer Perceptual Network (FBAIM-Net) for Enhanced Polyp Segmentation in Medical Imaging..- Advancing Free-breathing Cardiac Cine MRI: Retrospective Respiratory Motion Correction Via Kspace-and-Image Guided Diffusion Model..- Blood Cell Detection and Self-attention-based Mixed Attention Mechanism..- CellSpot: Deep Learning-Based Efficient Cell Center Detection in Microscopic Images..- Classification of dehiscence defects in titanium and zirconium dental implants..- CurSegNet: 3D Dental Model Segmentation Network Based on Curve Feature Aggregation..- DBrAL: A novel uncertainty-based active learning based on deep-broad learning for medical image classi cation..- EDPS-SST: Enhanced Dynamic Path Stitching with Structural Similarity Thresholding for Large-Scale Medical Image Stitching under Sparse Pixel Overlap..- Hop-Gated Graph Attention Network for ASD Diagnosis via PC-Based Graph RegularizationSparse Representation..- MISS: A Generative Pre-training and Fine-tuning Approach for Med-VQA..- MSD-HAM-Net: A Multi-modality Fusion Network of PET/CT Images for the Prognosis ofDLBCL Patients..- Multi-Modal Multi-Scale State Space Model for Medical Visual Question Answering..- Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling..- Point-based Weakly Supervised 2.5D Cell Segmentation..- Relative Local Signal Strength: the Impact of Normalization on the Analysis of Neuroimaging Data with Deep Learning..- SCANet: Dual Attention Network for Alzheimer’s Disease Diagnosis Based on Gated Residual andSpatial Asymmetry Mechanisms..- SCST: Spatial Consistent Swin Transformer for Multi-Focus Biomedical Microscopic ImageFusion..- KnowMIM: a self-supervised pre-training framework based on knowledge-guided maskedimage modeling for retinal vessel segmentation..- Transferability of Non-Contrastive Self-Supervised Learning to Chronic Wound Image Recognition..- Two-stage Medical Image-text Transfer with Supervised Contrastive Learning.