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.

Table 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.