Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VIII

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.

List of contents

.- 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.

Product details

• Assisted by: Kristína Malinovská (Editor), Jürgen Schmidhuber (Editor), Jürgen Schmidhuber et al (Editor), Igor V. Tetko (Editor), Michael Wand (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 17.10.2024
• EAN: 9783031723520
• ISBN: 978-3-0-3172352-0
• No. of pages: 463
• Dimensions: 155 mm x 26 mm x 235 mm
• Weight: 751 g
• Illustrations: XXXIV, 463 p. 128 illus., 123 illus. in color.
• Series: Lecture Notes in Computer Science
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT