Artificial Neural Networks and Machine Learning - ICANN 2024 [electronic resource] : 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VIII / edited by Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko.

-- 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.

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.

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