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检索条件"任意字段=2nd IAPR Workshop on Artificial Neural Networks in Pattern Recognition"

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Gaussian-Mixture neural networks 11th

Gaussian-Mixture Neural Networks

引用

11th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Meconcelli, Duccio Trentin, Edmondo Univ Siena DIISM Siena Italy

ISBN: (纸本)9783031716010;9783031716027

Density estimation is crucial to statistical pattern recognition, in both the supervised and unsupervised frameworks. It is still an open problem, due to its intrinsic difficulties and to the many shortcomings of statistical parametric and non-parametric techniques. artificial neural networks (ANN) have long been applied to the estimation of posterior probabilities for pattern classification (a simple supervised learning task), yet only a few attempts have been made to devise ANN-based density estimation algorithms. The paper proposes a novel algorithm for training an ANN from an unlabeled data sample of patterns randomly drawn from an underlying probability density function (PDF), say p(center dot), such that the ANN learns a robust non-parametric model of p(center dot). The algorithm leverages both the generalization capabilities of ANNs and the generality of the maximum-likelihood estimates of the parameters of Gaussian mixture models. Therefore, the proposed machine is termed Gaussian-mixture neural Network (GNN). The best selling points of the GNN lie in its simplicity and effectiveness. Preliminary experimental results are reported and analyzed that involve data samples of variable size randomly drawn from PDFs of known form, either unimodal or multimodal. The GNN favorably compares with established statistical and ANN-based estimators, scoring generally higher than its competitors over a range of evaluation metrics. The code used in the experiments is made publicly available online on GitHub.

关键词： Density estimation Gaussian mixture model artificial neural network non-parametric estimation deep learning

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A Hybrid Neuroevolutionary Approach to the Design of Convolutional neural networks for 2D and 3D Medical Image Segmentation 11th

A Hybrid Neuroevolutionary Approach to the Design of Convolu...

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11th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Ramesh, Nivedha Ashfaq, Tabish Kharma, Nawwaf Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9783031716010;9783031716027

The evolution of Convolutional neural networks (CNNs) has revolutionized medical image segmentation, yet designing optimal architectures remains a challenge. In this paper, we introduce a hybrid evolutionary algorithm that advances the design of CNNs for medical image segmentation. By integrating Cartesian Genetic Programming with Simulated Annealing, our approach efficiently explores the architectural design space, yielding CNN architectures that perform robustly across several medical imaging tasks. Our model demonstrated competitive performance on the DRIVE and Medical Segmentation Decathlon datasets, achieving Dice Similarity Coefficients up to 0.828, and excelling in computational efficiency with reduced GFLOP values. Notably, our algorithm's design eliminates the need for extensive pre-training, reducing computational overhead and enhancing its suitability for resource-constrained environments.

关键词： Medical Image Segmentation Evolutionary Algorithms Computational Efficiency neural Architecture Search (NAS)

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11th iapr TC3 workshop on artificial neural networks in pattern recognition, ANNPR 2024

11th IAPR TC3 workshop on Artificial Neural Networks in Patt...

引用

11th iapr TC3 workshop on artificial neural networks in pattern recognition, ANNPR 2024

ISBN: (纸本)9783031716010

The proceedings contain 27 papers. The special focus in this conference is on artificial neural networks in pattern recognition. The topics include: neural Decompiling of Tracr Transformers;pitfalls in Processing Infinite-Length Sequences with Popular Approaches for Sequential Data;robust Clustering with McDonald’s Beta-Liouville Mixture Models for Proportional Data;evaluating Support Vector Machines with Multiple Kernels by Random Search;Automatic Interpretation of 18F-Fluorocholine PET/CT Findings in Patients with Primary Hyperparathyroidism: A Novel Dataset with Benchmarks;a Hybrid Neuroevolutionary Approach to the Design of Convolutional neural networks for 2D and 3D Medical Image Segmentation;An Improved Pix2Pix GAN for Medical Image Generation;vision Transformer Features-Based Leukemia Classification;comparative Study of Deep Learning Models in Melanoma Detection;a Metaheuristic Optimization Based Deep Feature Selection for Oral Cancer Classification;machine Learning for Clinical Score Prediction from Longitudinal Dataset: A Case Study on Parkinson’s Disease;explaining Network Decision Provides Insights on the Causal Interaction Between Brain Regions in a Motor Imagery Task;Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via neural networks;VAeViT: Fusing Multi-views for Complete 3D Object recognition;leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos;palmprint Classification via Filter Faces and Feature Extraction;deep Multi-label Classification of Personality with Handwriting Analysis;license Plate Detection and Character recognition Using Deep Learning and Font Evaluation;experiments in Modeling Disagreement;deep Multiresolution Wavelet Transform for Speech Emotion Assessment of High-Risk Suicide Callers;Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming;Leveraging LSTM Embeddings for River Water Temperature Modeling;research on the Identification of Common Economic Shellfish in Jiang

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A Novel Representation of Graphical patterns for Graph Convolution networks 10th

A Novel Representation of Graphical Patterns for Graph Convo...

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10th iapr TC3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： Benini, Marco Bongini, Pietro Trentin, Edmondo Univ Siena DIISM Siena Italy

ISBN: (纸本)9783031206498;9783031206504

In the context of machine learning on graph data, graph deep learning has captured the attention of many researcher. Due to the promising results of deep learning models in the most diverse fields of application, great efforts have been made to replicate these successes when dealing with graph data. In this work, we propose a novel approach for processing graphs, with the intention of exploiting the already established capabilities of Convolutional neural networks (CNNs) in image processing. To this end we propose a new representation for graphs, called GrapHisto, in the form of unique tensors encapsulating the features of any given graph to then process the new data using the CNN paradigm.

关键词： GrapHisto Graph neural Network Graph Convolution Network

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Leveraging LSTM Embeddings for River Water Temperature Modeling 11th

Leveraging LSTM Embeddings for River Water Temperature Model...

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11th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Fankhauser, Benjamin Bigler, Vidushi Riesen, Kaspar Univ Bern Inst Comp Sci Bern Switzerland Bern Univ Appl Sci Inst Optimisat & Data Anal Biel Switzerland

ISBN: (纸本)9783031716010;9783031716027

River water temperature modeling is a major task in climate research. State-of-the-art methods for water temperature modeling deploy a transductive design, which makes it difficult to generalize to unseen water stations during test time. In the present paper, we isolate one of the common building blocks - a central LSTM, trained for each water station - and propose an embedding scheme in order to increase both the prediction accuracy and the amount of shared parameters and thus the generalization. The proposed embeddings are learned during training time. In an empirical evaluation we show that our method is able to reduce the RMSE by about five percentage points compared to the state-of-the-art reference method while decreasing the tuneable parameters by several orders of magnitude. We also provide a sample analysis of the embedding space of the catchment area of one specific river. Looking at the results of this qualitative analysis, we come to the conclusion that deploying an embedding in water temperature models is not only convincing to decrease the RMSE of water temperature predictions, but also enables better explainable deep learning models. Moreover, the proposed embedding technique opens up various unexplored applications in water temperature research.

关键词： Water Temperature Embedding LSTM Recurrent neural Network

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Medical Deepfake Detection using 3-Dimensional neural Learning 10th

Medical Deepfake Detection using 3-Dimensional Neural Learni...

引用

10th iapr TC3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： Sharafudeen, Misaj Chandra, S. S. Vinod Univ Kerala Dept Comp Sci Thiruvananthapuram Kerala India

ISBN: (纸本)9783031206498;9783031206504

In recent years, Generative Adversarial networks (GAN) have underlined the necessity for exercising caution in trusting digital information. Injection and removal of tumorous nodules from medical imaging modalities is one method of maneuvering deepfakes. The inability to acknowledge medical deepfakes can result in a substantial impact on healthcare procedures or even cause of death. With a systematic case study, this work seeks to address the detection of such assaults in lung CT (Computed Tomography) scans generated using CT-GANs. We experiment with machine learning methods and a novel 3-dimensional deep neural architecture on the topic of differentiating between tampered and untampered data. The proposed architecture on the CT-GAN dataset attained a remarkable accuracy of 91.57%, sensitivity of 91.42%, and specificity of 97.20%. Sectioned data cubes containing the affected region of interest seem to perform better compared to raw CT slices with a gain of approximately 20%. Furthermore, 3DCNN outperforms its 2dimensional counterpart as it extracts temporal features unlike the spatial relationship insufficient for medical data processing. The outcomes of this research reveal that nodule injection and removal manipulations in complicated CT slices may be recognized with a high degree of precision.

关键词： Deepfake detection Computed tomography Fake cancer nodules Machine learning Deep learning

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An Improved Pix2Pix GAN for Medical Image Generation 11th

An Improved Pix2Pix GAN for Medical Image Generation

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11th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Deng, Yanlin Ling, Jingwen Rao, Xiaqing Tan, Jun Fu, Xiaoyong Li, Sheng Sun Yat Sen Univ Sch Math Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510275 Peoples R China Sun Yat Sen Univ Dept Radiol Guangdong Prov Clin Res Ctr Canc State Key Lab Oncol South ChinaCanc Ctr Guangzhou 510060 Peoples R China

ISBN: (纸本)9783031716010;9783031716027

Generating missing modality medical images using deep learning is crucial for disease diagnosis, treatment planning, and medical education. With the rapid development of deep learning, Generative Adversarial networks (GANs) have shown significant potential by producing realistic, high-quality images. However, GANs face challenges such as blurred organ edges, diffuse tissue boundaries, and failure to maintain the target modality's style. This study improves the classic GANs model by introducing the Channel Attention Module (CBAM) into the generator's Residual Block, enhancing feature extraction and encoding capabilities. We integrate the VGG16 network to enhance the depiction of image edge intricacies and introduce a histogram chi-square comparison loss to tackle the challenges associated with bone structure generation, stemming from data imbalance. To enhance image clarity, we include a gradient loss term in the loss function. Additionally, real CT images are input into the generator to help the model learn the target modality's style features. Using Conditional GANs, mismatched data pairs are input into the discriminator with false labels to improve content matching sensitivity. Extensive experiments on public brain datasets demonstrate that our method significantly enhances GANs's image generation capabilities, producing images that excel in both visual quality and quantitative metrics.

关键词： Medical Images Cross-modal Image Generation GANs

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13th iapr-TC-15 International workshop on Graph-Based Representations in pattern recognition, GbRPR 2023

13th IAPR-TC-15 International Workshop on Graph-Based Repres...

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13th iapr-TC-15 International workshop on Graph-Based Representations in pattern recognition, GbRPR 2023

ISBN: (纸本)9783031427947

The proceedings contain 16 papers. The special focus in this conference is on . The topics include: Maximal Independent Sets for Pooling in Graph neural networks;detecting Abnormal Communication patterns in IoT networks Using Graph neural networks;cell Segmentation of in situ Transcriptomics Data Using Signed Graph Partitioning;graph-Based Representation for Multi-image Super-Resolution;reducing the Computational Complexity of the Eccentricity Transform of a Tree;graph-Based Deep Learning on the Swiss River Network;minimum Spanning Set Selection in Graph Kernels;graph-Based vs. Vector-Based Classification: A Fair Comparison;a Practical Algorithm for Max-Norm Optimal Binary Labeling of Graphs;an Efficient Entropy-Based Graph Kernel;GNN-DES: A New End-to-End Dynamic Ensemble Selection Method Based on Multi-label Graph neural Network;C2N-ABDP: Cluster-to-Node Attention-Based Differentiable Pooling;splitting Structural and Semantic Knowledge in Graph Autoencoders for Graph Regression;graph Normalizing Flows to Pre-image Free Machine Learning for Regression.

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Se²NAS: Self-Semi-Supervised architecture optimization for Semantic Segmentation 26

Se<SUP>2</SUP>NAS: Self-Semi-Supervised architecture optimiz...

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26th International Conference on pattern recognition / 8th International workshop on Image Mining - Theory and Applications (IMTA)

作者： Pauletto, Loic Amini, Massih-Reza Winckler, Nicolas ATOS Grenoble France Univ Grenoble Alpes Grenoble France

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this paper, we propose a neural Architecture Search strategy based on self supervision and semi-supervised learning for the task of semantic segmentation. Our approach builds an optimized neural network (NN) model for this task by solving a jigsaw pretext problem identified by self-supervised learning over unlabeled training data, and, leveraging the structure of the unlabeled data with semi-supervised learning. Dynamic routing with a gradient descent approach is used to find the architecture of the NN model. Experiments on the Cityscapes and PASCAL VOC 2012 datasets show that the found neural network is four times more efficient than a state-of-the-art hand designed NN model in terms of floating-point operations.

关键词： Semantic segmentation Training data artificial neural networks Self-supervised learning Semisupervised learning Search problems Routing

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Natural Language Processing-based Model for Log Anomaly Detection 2

Natural Language Processing-based Model for Log Anomaly Dete...

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2nd IEEE International Conference on Software Engineering and artificial Intelligence (SEAI) / 7th International workshop on pattern recognition (IWPR)

作者： Li, Zezhou Zhang, Jing Zhang, Xianbo Lin, Feng Wang, Chao Cai, Xingye JD Tech Beijing Peoples R China

ISBN: (数字)9781665482233

ISBN: (纸本)9781665482233

Logs are widely used in IT industry and the anomaly detection of logs is essential to identify the running status of systems. Conventional methods solving this problem require sophisticated rule-based regulations and intensive labor input. In this paper, we propose a new model based on natural language processing techniques. In order to modify the feature extraction and to improve the vector quality of log templates, Part-of-Speech (PoS) and Named Entity recognition (NER) are employed in our model, which leads to the less involvement of regulation-based rule and a modification of the template vector thanks to the weight vector by NER. The PoS property of each token in the template is firstly analyzed, which also reduces labor involvement and helps for better weight allocation. The weight investigation on tokens of the template is introduced to modify the template vector. And the final detection based on the modified vector of templates is realized by deep neural networks (DNNs). The effectiveness of our model is tested on three datasets, and compared with two state-ofthe-art models. The evaluation results prove that our model achieves better log anomaly detection.

关键词： log anomaly detection natural language processing deep neural networks

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