Artificial Neural Networks and Machine Learning – ICANN 2024

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

Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko (Herausgeber)

Buch | Softcover

XXXIV, 463 Seiten

2024
Springer International Publishing (Verlag)
9783031723520 (ISBN)

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The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17-20, 2024.

The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics:

Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning.

Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods.

Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision.

Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning.

Part V - graph neural networks; and large language models.

Part VI - multimodality; federated learning; and time series processing.

Part VII - speech processing; natural language processing; and language modeling.

Part VIII - biosignal processing in medicine and physiology; and medical image processing.

Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security.

Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

.- 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 through
ICD 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 Similarity
Graph (DE-PSG).

.- Meteorological Data based Detection of Stroke using Machine Learning Techniques.

.- OFNN-UNI: Enhanced Optimized Fuzzy Neural Networks based on Unineurons for Advanced Sepsis
Classification.

.- 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 Regularization
Sparse 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 of
DLBCL 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 and
Spatial Asymmetry Mechanisms.

.- SCST: Spatial Consistent Swin Transformer for Multi-Focus Biomedical Microscopic Image
Fusion.

.- KnowMIM: a self-supervised pre-training framework based on knowledge-guided masked
image 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.

Erscheinungsdatum	17.09.2024
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	XXXIV, 463 p. 128 illus., 123 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Artificial Intelligence • classification • Deep learning • generative models • graph neural networks • Image Processing • Large Language Models • machine learning • Neural networks • Reinforcement Learning • reservoir computing • Robotics • spiking neural networks
ISBN-13	9783031723520 / 9783031723520
Zustand	Neuware