Artificial Neural Networks and Machine Learning - ICANN 2024 : 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VIII (Lecture Notes in Computer Science) （2024）

Wand, Michael (EDT)/ Malinovská, Kristína (EDT)/ Schmidhuber, Jürgen (EDT)

Springer International Publishing AG（2024/10発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 463 p.
• 言語 ENG
• 商品コード 9783031723520
• DDC分類 006.32

Full Description

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.

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.