Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries [[electronic resource] ] : 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I / / edited by Alessandro Crimi, Spyridon Bakas |
Autore | Crimi Alessandro |
Edizione | [1st ed. 2022.] |
Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
Descrizione fisica | 1 online resource (XXI, 489 p. 171 illus., 134 illus. in color.) |
Disciplina | 006.37 |
Collana | Lecture Notes in Computer Science |
Soggetto topico |
Computer vision
Artificial intelligence Computer engineering Computer networks Application software Computer Vision Artificial Intelligence Computer Engineering and Networks Computer and Information Systems Applications |
Soggetto non controllato |
artificial intelligence
bioinformatics computer science computer systems computer vision education image analysis image processing image segmentation learning machine learning medical images neural networks pattern recognition segmentation methods software design software engineering software quality validation verification and validation |
ISBN | 3-031-08999-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Supervoxel Merging towards Brain Tumor Segmentation -- Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma -- Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks -- Optimization of Deep Learning based Brain Extraction in MRI for Low Resource Environments. Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task -- Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation -- BRATS2021: exploring each sequence in multi-modal input for baseline U-net performance -- Automatic Brain Tumor Segmentation using Multi-scale Features and Attention Mechanism -- Simple and Fast Convolutional Neural Network applied to median cross sections for predicting the presence of MGMT promoter methylation in FLAIR MRI scans -- MSViT: Multi Scale Vision Transformer forBiomedical Image Segmentation -- Unsupervised Multimodal -- HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation -- Multimodal Brain Tumor Segmentation Algorithm -- Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images -- Multi-plane UNet++ Ensemble for Glioblastoma Segmentation -- Multimodal Brain Tumor Segmentation using Modified UNet Architecture -- A video data based transfer learning approach for classification of MGMT status in brain tumor MR images -- Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021 -- 3D MRI brain tumour segmentation with autoencoder regularization and Hausdorff distance loss function -- 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge -- Cascaded training pipeline for 3D brain tumor segmentation -- nnU-Net with Region-based Training and Loss Ensembles for Brain Tumor Segmentation -- Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining -- Automatic segmentation of brain tumor using 3D convolutional neural networks -- Hierarchical and Global Modality Interaction for Brain Tumor Segmentation -- Ensemble Outperforms Single Models in Brain Tumor Segmentation -- Brain Tumor Segmentation using UNet-Context Encoding Network -- Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI. |
Record Nr. | UNINA-9910585786203321 |
Crimi Alessandro
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Cham, : Springer Nature, 2022 | ||
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Lo trovi qui: Univ. Federico II | ||
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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries [[electronic resource] ] : 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I / / edited by Alessandro Crimi, Spyridon Bakas |
Autore | Crimi Alessandro |
Edizione | [1st ed. 2022.] |
Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
Descrizione fisica | 1 online resource (XXI, 489 p. 171 illus., 134 illus. in color.) |
Disciplina | 006.37 |
Collana | Lecture Notes in Computer Science |
Soggetto topico |
Computer vision
Artificial intelligence Computer engineering Computer networks Application software Computer Vision Artificial Intelligence Computer Engineering and Networks Computer and Information Systems Applications |
Soggetto non controllato |
artificial intelligence
bioinformatics computer science computer systems computer vision education image analysis image processing image segmentation learning machine learning medical images neural networks pattern recognition segmentation methods software design software engineering software quality validation verification and validation |
ISBN | 3-031-08999-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Supervoxel Merging towards Brain Tumor Segmentation -- Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Modeling multi-annotator uncertainty as multi-class segmentation problem -- Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma -- Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks -- Optimization of Deep Learning based Brain Extraction in MRI for Low Resource Environments. Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task -- Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation -- BRATS2021: exploring each sequence in multi-modal input for baseline U-net performance -- Automatic Brain Tumor Segmentation using Multi-scale Features and Attention Mechanism -- Simple and Fast Convolutional Neural Network applied to median cross sections for predicting the presence of MGMT promoter methylation in FLAIR MRI scans -- MSViT: Multi Scale Vision Transformer forBiomedical Image Segmentation -- Unsupervised Multimodal -- HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation -- Multimodal Brain Tumor Segmentation Algorithm -- Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images -- Multi-plane UNet++ Ensemble for Glioblastoma Segmentation -- Multimodal Brain Tumor Segmentation using Modified UNet Architecture -- A video data based transfer learning approach for classification of MGMT status in brain tumor MR images -- Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021 -- 3D MRI brain tumour segmentation with autoencoder regularization and Hausdorff distance loss function -- 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge -- Cascaded training pipeline for 3D brain tumor segmentation -- nnU-Net with Region-based Training and Loss Ensembles for Brain Tumor Segmentation -- Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining -- Automatic segmentation of brain tumor using 3D convolutional neural networks -- Hierarchical and Global Modality Interaction for Brain Tumor Segmentation -- Ensemble Outperforms Single Models in Brain Tumor Segmentation -- Brain Tumor Segmentation using UNet-Context Encoding Network -- Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI. |
Record Nr. | UNISA-996483157303316 |
Crimi Alessandro
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Cham, : Springer Nature, 2022 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Deep Learning in Medical Image Analysis |
Autore | Zhang Yudong |
Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
Descrizione fisica | 1 electronic resource (458 p.) |
Soggetto non controllato |
interpretable/explainable machine learning
image classification image processing machine learning models white box black box cancer prediction deep learning multimodal learning convolutional neural networks autism fMRI texture analysis melanoma glcm matrix machine learning classifiers explainability explainable AI XAI medical imaging diagnosis ARMD change detection unsupervised learning microwave breast imaging image reconstruction tumor detection digital pathology whole slide image processing multiple instance learning deep learning classification HER2 medical images transfer learning optimizers neo-adjuvant treatment tumour cellularity cancer breast cancer diagnostics imaging computation artificial intelligence 3D segmentation active surface discriminant analysis PET imaging medical image analysis brain tumor cervical cancer colon cancer lung cancer computer vision musculoskeletal images lung disease detection taxonomy convolutional neural network CycleGAN data augmentation dermoscopic images domain transfer macroscopic images skin lesion segmentation infection detection COVID-19 X-ray images bayesian inference shifted-scaled dirichlet distribution MCMC gibbs sampling object detection surgical tools open surgery egocentric camera computers in medicine segmentation MRI ECG signal detection portable monitoring devices 1D-convolutional neural network medical image segmentation domain adaptation meta-learning U-Net computed tomography (CT) magnetic resonance imaging (MRI) low-dose sparse-angle quantitative comparison |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910557435103321 |
Zhang Yudong
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
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Lo trovi qui: Univ. Federico II | ||
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