Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops : ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8-12, 2023, Proceedings
| Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops : ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8-12, 2023, Proceedings |
| Autore | Celebi M. Emre |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (397 pages) |
| Altri autori (Persone) |
SalekinSirajus
KimHyunwoo AlbarqouniShadi BarataCatarina HalpernAllan TschandlPhilipp CombaliaMarc LiuYuan ZamzmiGhada |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-031-47401-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Workshop Editors -- ISIC Preface -- ISIC 2023 Organization -- Care-AI 2023 Preface -- Care-AI 2023 Organization -- MedAGI 2023 Preface -- MedAGI 2023 Organization -- DeCaF 2023 Preface -- DeCaF 2023 Organization -- Contents -- Proceedings of the Eighth International Skin Imaging Collaboration Workshop (ISIC 2023) -- Continual-GEN: Continual Group Ensembling for Domain-agnostic Skin Lesion Classification -- 1 Introduction -- 2 Continual-GEN -- 3 Experiments and Results -- 4 Conclusion -- References -- Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation -- 1 Introduction -- 2 Method -- 2.1 Generalizable Dataset Distillation -- 2.2 Distillation Process -- 2.3 Communication-Efficient Federated Learning -- 3 Experiment -- 3.1 Datasets and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Comparison of State-of-the-Arts -- 3.4 Detailed Analysis -- 4 Conclusion -- References -- AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets -- 1 Introduction -- 2 Methodology -- 2.1 Basic ViT -- 2.2 AViT -- 3 Experiments -- 4 Conclusion -- References -- Test-Time Selection for Robust Skin Lesion Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Results -- 5 Conclusion -- References -- Global and Local Explanations for Skin Cancer Diagnosis Using Prototypes -- 1 Introduction -- 2 Proposed Approach -- 2.1 Clustering -- 2.2 Global Prototypes -- 2.3 Local Prototypes -- 2.4 Pruning and Final Classifier -- 2.5 Visual Explanations -- 3 Experimental Setup -- 4 Results -- 5 Conclusions -- References -- Evidence-Driven Differential Diagnosis of Malignant Melanoma -- 1 Introduction -- 2 Method -- 3 Experiments -- 3.1 Data -- 3.2 Experimental Settings -- 4 Results and Discussion -- 5 Conclusion -- References.
Proceedings of the First Clinically-Oriented and Responsible AI for Medical Data Analysis (Care-AI 2023) Workshop -- An Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer's Disease -- 1 Introduction -- 2 Methods -- 2.1 Study Population -- 2.2 Data Preprocessing -- 2.3 Explainable Boosting Machine (EBM) -- 2.4 Proposed Extension -- 2.5 Extraction of the DL-Biomarkers -- 3 Experiments -- 3.1 Validation Study -- 3.2 EBM Using DL-Biomarkers -- 3.3 Baseline Methods -- 4 Results -- 4.1 Glo-CNN and ROIs -- 4.2 Comparison Study -- 5 Discussion and Conclusions -- References -- Generating Chinese Radiology Reports from X-Ray Images: A Public Dataset and an X-ray-to-Reports Generation Method -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 3.1 CN-CXR Dataset -- 3.2 CN-RadGraph Dataset -- 4 Method -- 5 Results and Analysis -- 5.1 Pre-processing and Evaluation -- 5.2 Performance and Comparisons -- References -- Gradient Self-alignment in Private Deep Learning -- 1 Introduction -- 2 Background and Related Work -- 3 Methodology -- 3.1 DP-SGD with a Cosine Similarity Filter -- 3.2 DP-SGD with Dimension-Filtered Cosine Similarity Filter -- 4 Experiments and Discussion -- 4.1 Experimental Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Cellular Features Based Interpretable Network for Classifying Cell-Of-Origin from Whole Slide Images for Diffuse Large B-cell Lymphoma Patients -- 1 Introduction -- 2 Methodology -- 2.1 Nuclei Segmentation and Classification -- 2.2 Cellular Feature Extraction -- 2.3 AMIL Model Training -- 2.4 CellFiNet Interpretability -- 2.5 Benchmark Methods -- 3 Experiments and Results -- 3.1 Data -- 3.2 Results -- 4 Interpretability -- 5 Conclusion and Discussion -- References. Multimodal Learning for Improving Performance and Explainability of Chest X-Ray Classification -- 1 Introduction -- 2 Method -- 2.1 Classification Performance Experiments -- 2.2 Explainability Experiments -- 3 Results -- 3.1 Classification Performance Results -- 3.2 Explainability Results -- 4 Conclusion -- References -- Proceedings of the First International Workshop on Foundation Models for Medical Artificial General Intelligence (MedAGI 2023) -- Cross-Task Attention Network: Improving Multi-task Learning for Medical Imaging Applications -- 1 Introduction -- 2 Methods and Materials -- 2.1 Cross-Task Attention Network (CTAN) -- 2.2 Training Details -- 2.3 Evaluation -- 2.4 Datasets -- 3 Experiments and Results -- 4 Discussion -- References -- Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Segmentation and Boundary Prior Maps -- 3.2 Augmenting Input Images -- 3.3 Model Training with SAM-Augmented Images -- 3.4 Model Deployment with SAM-Augmented Images -- 4 Experiments and Results -- 4.1 Datasets and Setups -- 4.2 Polyp Segmentation on Five Datasets -- 4.3 Cell Segmentation on the MoNuSeg Dataset -- 4.4 Gland Segmentation on the GlaS Dataset -- 5 Conclusions -- References -- Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging -- 1 Introduction -- 2 Related Works -- 2.1 UniverSeg -- 2.2 Prostate MR Segmentation -- 3 Experiments -- 3.1 Datasets -- 3.2 UniverSeg Inference -- 3.3 nnUNet -- 3.4 Empirical Evaluation -- 4 Results -- 4.1 Computational Resource -- 4.2 Segmentation Performance -- 4.3 Conclusion -- References -- GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as a Plug-and-Play Transductive Model for Medical Image Analysis -- 1 Introduction -- 2 Approach -- 2.1 Theoretical Analyses. 2.2 Prompt Construction -- 2.3 Workflow and Use Cases -- 3 Experiments -- 3.1 On Detecting Prediction Errors -- 3.2 On Improving Classification Accuracy -- 3.3 Ablation Studies -- 4 Discussion and Conclusions -- References -- SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology -- 1 Introduction -- 2 Method -- 2.1 Pathology Encoder -- 2.2 Class Prompts -- 2.3 Optimization -- 3 Experiments -- 3.1 Dataset -- 3.2 Results -- 4 Conclusion -- References -- Multi-task Cooperative Learning via Searching for Flat Minima -- 1 Introduction -- 2 Method -- 2.1 Bi-Level Optimization for Cooperative Two-Task Learning -- 2.2 Finding Flat Minima via Injecting Noise -- 3 Experiments -- 3.1 Dataset -- 3.2 Results on MNIST Dataset -- 3.3 Comparison on REFUGE2018 Dataset -- 3.4 Comparison on HRF-AV Dataset -- 4 Conclusion -- References -- MAP: Domain Generalization via Meta-Learning on Anatomy-Consistent Pseudo-Modalities -- 1 Introduction -- 2 Methods -- 2.1 Problem Definition -- 2.2 Pseudo-modality Synthesis -- 2.3 Meta-learning on Anatomy Consistent Image Space -- 2.4 Structural Correlation Constraints -- 2.5 Experimental Settings -- 3 Results -- 4 Conclusion -- References -- A General Computationally-Efficient 3D Reconstruction Pipeline for Multiple Images with Point Clouds -- 1 Introduction -- 2 Related Works -- 3 Method -- 4 Implementation -- 5 Quantitative Results -- 6 Conclusion and Future Work -- References -- GPC: Generative and General Pathology Image Classifier -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 Network Architecture -- 3 Experiments -- 3.1 Datasets -- 3.2 Comparative Models -- 3.3 Experimental Design -- 3.4 Training Details -- 3.5 Metrics -- 4 Results and Discussion -- 5 Conclusions -- References. Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Experiments -- 5 Conclusions -- References -- Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification -- 1 Introduction -- 2 Related Works -- 2.1 Concept Bottleneck Models -- 2.2 Large Language Models -- 3 Method -- 3.1 Preliminary -- 3.2 Concept Selection with Visual Activation Score -- 4 Experiments -- 4.1 Experimental Results -- 5 Analysis -- 5.1 Analysis on a Visual Activation Score V(c) -- 5.2 Analysis on Target Image Set X -- 5.3 Qualitative Examples -- 6 Conclusion -- References -- SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation -- 1 Introduction -- 2 Experimental Settings -- 3 Surgical Instruments Segmentation with Prompts -- 4 Robustness Under Data Corruption -- 5 Automatic Surgical Scene Segmentation -- 6 Parameter-Efficient Finetuning with Low-Rank Adaptation -- 7 Conclusion -- References -- Evaluation and Improvement of Segment Anything Model for Interactive Histopathology Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Overview of Segment Anything Model (SAM) -- 2.2 SAM Fine-Tuning Scenarios -- 2.3 Decoder Architecture Modification -- 3 Experiments -- 3.1 Data Description -- 3.2 Implementation Details -- 4 Results -- 4.1 Zero-Shot Performance -- 4.2 Fine-Tuned SAM Performance -- 4.3 Comparison Between SAM and SOTA Interactive Methods -- 4.4 Modified SAM Decoder Performance -- 5 Conclusion -- References -- Task-Driven Prompt Evolution for Foundation Models -- 1 Introduction -- 1.1 Our Approach -- 1.2 Contributions -- 2 Methodology -- 2.1 Prompt Optimization by Oracle Scoring -- 2.2 Learning to Score -- 3 Experiments and Results -- 3.1 Dataset Description -- 3.2 Segmentation Regressor. 3.3 Prompt Optimization. |
| Record Nr. | UNISA-996565867403316 |
Celebi M. Emre
|
||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops : ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8-12, 2023, Proceedings
| Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops : ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8-12, 2023, Proceedings |
| Autore | Celebi M. Emre |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (397 pages) |
| Altri autori (Persone) |
SalekinSirajus
KimHyunwoo AlbarqouniShadi BarataCatarina HalpernAllan TschandlPhilipp CombaliaMarc LiuYuan ZamzmiGhada |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-031-47401-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Workshop Editors -- ISIC Preface -- ISIC 2023 Organization -- Care-AI 2023 Preface -- Care-AI 2023 Organization -- MedAGI 2023 Preface -- MedAGI 2023 Organization -- DeCaF 2023 Preface -- DeCaF 2023 Organization -- Contents -- Proceedings of the Eighth International Skin Imaging Collaboration Workshop (ISIC 2023) -- Continual-GEN: Continual Group Ensembling for Domain-agnostic Skin Lesion Classification -- 1 Introduction -- 2 Continual-GEN -- 3 Experiments and Results -- 4 Conclusion -- References -- Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation -- 1 Introduction -- 2 Method -- 2.1 Generalizable Dataset Distillation -- 2.2 Distillation Process -- 2.3 Communication-Efficient Federated Learning -- 3 Experiment -- 3.1 Datasets and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Comparison of State-of-the-Arts -- 3.4 Detailed Analysis -- 4 Conclusion -- References -- AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets -- 1 Introduction -- 2 Methodology -- 2.1 Basic ViT -- 2.2 AViT -- 3 Experiments -- 4 Conclusion -- References -- Test-Time Selection for Robust Skin Lesion Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Results -- 5 Conclusion -- References -- Global and Local Explanations for Skin Cancer Diagnosis Using Prototypes -- 1 Introduction -- 2 Proposed Approach -- 2.1 Clustering -- 2.2 Global Prototypes -- 2.3 Local Prototypes -- 2.4 Pruning and Final Classifier -- 2.5 Visual Explanations -- 3 Experimental Setup -- 4 Results -- 5 Conclusions -- References -- Evidence-Driven Differential Diagnosis of Malignant Melanoma -- 1 Introduction -- 2 Method -- 3 Experiments -- 3.1 Data -- 3.2 Experimental Settings -- 4 Results and Discussion -- 5 Conclusion -- References.
Proceedings of the First Clinically-Oriented and Responsible AI for Medical Data Analysis (Care-AI 2023) Workshop -- An Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer's Disease -- 1 Introduction -- 2 Methods -- 2.1 Study Population -- 2.2 Data Preprocessing -- 2.3 Explainable Boosting Machine (EBM) -- 2.4 Proposed Extension -- 2.5 Extraction of the DL-Biomarkers -- 3 Experiments -- 3.1 Validation Study -- 3.2 EBM Using DL-Biomarkers -- 3.3 Baseline Methods -- 4 Results -- 4.1 Glo-CNN and ROIs -- 4.2 Comparison Study -- 5 Discussion and Conclusions -- References -- Generating Chinese Radiology Reports from X-Ray Images: A Public Dataset and an X-ray-to-Reports Generation Method -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 3.1 CN-CXR Dataset -- 3.2 CN-RadGraph Dataset -- 4 Method -- 5 Results and Analysis -- 5.1 Pre-processing and Evaluation -- 5.2 Performance and Comparisons -- References -- Gradient Self-alignment in Private Deep Learning -- 1 Introduction -- 2 Background and Related Work -- 3 Methodology -- 3.1 DP-SGD with a Cosine Similarity Filter -- 3.2 DP-SGD with Dimension-Filtered Cosine Similarity Filter -- 4 Experiments and Discussion -- 4.1 Experimental Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Cellular Features Based Interpretable Network for Classifying Cell-Of-Origin from Whole Slide Images for Diffuse Large B-cell Lymphoma Patients -- 1 Introduction -- 2 Methodology -- 2.1 Nuclei Segmentation and Classification -- 2.2 Cellular Feature Extraction -- 2.3 AMIL Model Training -- 2.4 CellFiNet Interpretability -- 2.5 Benchmark Methods -- 3 Experiments and Results -- 3.1 Data -- 3.2 Results -- 4 Interpretability -- 5 Conclusion and Discussion -- References. Multimodal Learning for Improving Performance and Explainability of Chest X-Ray Classification -- 1 Introduction -- 2 Method -- 2.1 Classification Performance Experiments -- 2.2 Explainability Experiments -- 3 Results -- 3.1 Classification Performance Results -- 3.2 Explainability Results -- 4 Conclusion -- References -- Proceedings of the First International Workshop on Foundation Models for Medical Artificial General Intelligence (MedAGI 2023) -- Cross-Task Attention Network: Improving Multi-task Learning for Medical Imaging Applications -- 1 Introduction -- 2 Methods and Materials -- 2.1 Cross-Task Attention Network (CTAN) -- 2.2 Training Details -- 2.3 Evaluation -- 2.4 Datasets -- 3 Experiments and Results -- 4 Discussion -- References -- Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Segmentation and Boundary Prior Maps -- 3.2 Augmenting Input Images -- 3.3 Model Training with SAM-Augmented Images -- 3.4 Model Deployment with SAM-Augmented Images -- 4 Experiments and Results -- 4.1 Datasets and Setups -- 4.2 Polyp Segmentation on Five Datasets -- 4.3 Cell Segmentation on the MoNuSeg Dataset -- 4.4 Gland Segmentation on the GlaS Dataset -- 5 Conclusions -- References -- Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging -- 1 Introduction -- 2 Related Works -- 2.1 UniverSeg -- 2.2 Prostate MR Segmentation -- 3 Experiments -- 3.1 Datasets -- 3.2 UniverSeg Inference -- 3.3 nnUNet -- 3.4 Empirical Evaluation -- 4 Results -- 4.1 Computational Resource -- 4.2 Segmentation Performance -- 4.3 Conclusion -- References -- GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as a Plug-and-Play Transductive Model for Medical Image Analysis -- 1 Introduction -- 2 Approach -- 2.1 Theoretical Analyses. 2.2 Prompt Construction -- 2.3 Workflow and Use Cases -- 3 Experiments -- 3.1 On Detecting Prediction Errors -- 3.2 On Improving Classification Accuracy -- 3.3 Ablation Studies -- 4 Discussion and Conclusions -- References -- SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology -- 1 Introduction -- 2 Method -- 2.1 Pathology Encoder -- 2.2 Class Prompts -- 2.3 Optimization -- 3 Experiments -- 3.1 Dataset -- 3.2 Results -- 4 Conclusion -- References -- Multi-task Cooperative Learning via Searching for Flat Minima -- 1 Introduction -- 2 Method -- 2.1 Bi-Level Optimization for Cooperative Two-Task Learning -- 2.2 Finding Flat Minima via Injecting Noise -- 3 Experiments -- 3.1 Dataset -- 3.2 Results on MNIST Dataset -- 3.3 Comparison on REFUGE2018 Dataset -- 3.4 Comparison on HRF-AV Dataset -- 4 Conclusion -- References -- MAP: Domain Generalization via Meta-Learning on Anatomy-Consistent Pseudo-Modalities -- 1 Introduction -- 2 Methods -- 2.1 Problem Definition -- 2.2 Pseudo-modality Synthesis -- 2.3 Meta-learning on Anatomy Consistent Image Space -- 2.4 Structural Correlation Constraints -- 2.5 Experimental Settings -- 3 Results -- 4 Conclusion -- References -- A General Computationally-Efficient 3D Reconstruction Pipeline for Multiple Images with Point Clouds -- 1 Introduction -- 2 Related Works -- 3 Method -- 4 Implementation -- 5 Quantitative Results -- 6 Conclusion and Future Work -- References -- GPC: Generative and General Pathology Image Classifier -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 Network Architecture -- 3 Experiments -- 3.1 Datasets -- 3.2 Comparative Models -- 3.3 Experimental Design -- 3.4 Training Details -- 3.5 Metrics -- 4 Results and Discussion -- 5 Conclusions -- References. Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Experiments -- 5 Conclusions -- References -- Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification -- 1 Introduction -- 2 Related Works -- 2.1 Concept Bottleneck Models -- 2.2 Large Language Models -- 3 Method -- 3.1 Preliminary -- 3.2 Concept Selection with Visual Activation Score -- 4 Experiments -- 4.1 Experimental Results -- 5 Analysis -- 5.1 Analysis on a Visual Activation Score V(c) -- 5.2 Analysis on Target Image Set X -- 5.3 Qualitative Examples -- 6 Conclusion -- References -- SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation -- 1 Introduction -- 2 Experimental Settings -- 3 Surgical Instruments Segmentation with Prompts -- 4 Robustness Under Data Corruption -- 5 Automatic Surgical Scene Segmentation -- 6 Parameter-Efficient Finetuning with Low-Rank Adaptation -- 7 Conclusion -- References -- Evaluation and Improvement of Segment Anything Model for Interactive Histopathology Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Overview of Segment Anything Model (SAM) -- 2.2 SAM Fine-Tuning Scenarios -- 2.3 Decoder Architecture Modification -- 3 Experiments -- 3.1 Data Description -- 3.2 Implementation Details -- 4 Results -- 4.1 Zero-Shot Performance -- 4.2 Fine-Tuned SAM Performance -- 4.3 Comparison Between SAM and SOTA Interactive Methods -- 4.4 Modified SAM Decoder Performance -- 5 Conclusion -- References -- Task-Driven Prompt Evolution for Foundation Models -- 1 Introduction -- 1.1 Our Approach -- 1.2 Contributions -- 2 Methodology -- 2.1 Prompt Optimization by Oracle Scoring -- 2.2 Learning to Score -- 3 Experiments and Results -- 3.1 Dataset Description -- 3.2 Segmentation Regressor. 3.3 Prompt Optimization. |
| Record Nr. | UNINA-9910767528603321 |
|
Celebi M. Emre
|
||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Medical Image Learning with Limited and Noisy Data [[electronic resource] ] : Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Zhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang
| Medical Image Learning with Limited and Noisy Data [[electronic resource] ] : Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Zhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang |
| Autore | Xue Zhiyun |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (274 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
AntaniSameer
ZamzmiGhada YangFeng RajaramanSivaramakrishnan HuangSharon Xiaolei LinguraruMarius George LiangZhaohui |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-44917-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Efficient Annotation and Training Strategies -- Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-quality Annotations -- ScribSD: Scribble-supervised Fetal MRI Segmentation based on Simultaneous Feature and Prediction Self-Distillation -- Label-efficient Contrastive Learning-based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images -- Affordable Graph Neural Network Framework using Topological Graph Contraction -- Approaches for Noisy, Missing, and Low Quality Data -- Dual-domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-angle Reconstruction of Low-dose Cardiac SPECT -- A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging -- COVID-19 Lesion Segmentation Framework for the Contrast-enhanced CT in the Absence of Contrast-enhanced CT Annotation -- Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Image -- Unsupervised, Self-supervised, and Contrastive Learning -- Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection -- FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation -- Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy -- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning -- SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction -- Robust Unsupervised Image to Template Registration Without Image Similarity Los -- A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue -- Weakly-supervised, Semi-supervised, and Multitask Learning -- Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification -- Exigent Examiner and Mean Teacher: An Advanced 3D CNN-based Semi-Supervised Brain Tumor Segmentation Framework -- Extremely Weakly-supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation -- Multi-Task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Image -- Active Learning -- Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach -- Test-time Augmentation-based Active Learning and Self-training for Label-efficient Segmentation -- Active Transfer Learning for 3D Hippocampus Segmentation -- Transfer Learning -- Using Training Samples as Transitive Information Bridges in Predicted 4D MRI -- To Pretrain or not to Pretrain? A Case Study of Domain-Specific Pretraining for Semantic Segmentation in Histopathology -- Large-scale Pretraining on Pathological Images for Fine-tuning of Small Pathological Benchmarks. |
| Record Nr. | UNISA-996558470503316 |
|
Xue Zhiyun
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Medical Image Learning with Limited and Noisy Data : Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Zhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang
| Medical Image Learning with Limited and Noisy Data : Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Zhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang |
| Autore | Xue Zhiyun |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (274 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
AntaniSameer
ZamzmiGhada YangFeng RajaramanSivaramakrishnan HuangSharon Xiaolei LinguraruMarius George LiangZhaohui |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-44917-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Efficient Annotation and Training Strategies -- Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-quality Annotations -- ScribSD: Scribble-supervised Fetal MRI Segmentation based on Simultaneous Feature and Prediction Self-Distillation -- Label-efficient Contrastive Learning-based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images -- Affordable Graph Neural Network Framework using Topological Graph Contraction -- Approaches for Noisy, Missing, and Low Quality Data -- Dual-domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-angle Reconstruction of Low-dose Cardiac SPECT -- A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging -- COVID-19 Lesion Segmentation Framework for the Contrast-enhanced CT in the Absence of Contrast-enhanced CT Annotation -- Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Image -- Unsupervised, Self-supervised, and Contrastive Learning -- Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection -- FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation -- Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy -- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning -- SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction -- Robust Unsupervised Image to Template Registration Without Image Similarity Los -- A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue -- Weakly-supervised, Semi-supervised, and Multitask Learning -- Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification -- Exigent Examiner and Mean Teacher: An Advanced 3D CNN-based Semi-Supervised Brain Tumor Segmentation Framework -- Extremely Weakly-supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation -- Multi-Task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Image -- Active Learning -- Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach -- Test-time Augmentation-based Active Learning and Self-training for Label-efficient Segmentation -- Active Transfer Learning for 3D Hippocampus Segmentation -- Transfer Learning -- Using Training Samples as Transitive Information Bridges in Predicted 4D MRI -- To Pretrain or not to Pretrain? A Case Study of Domain-Specific Pretraining for Semantic Segmentation in Histopathology -- Large-scale Pretraining on Pathological Images for Fine-tuning of Small Pathological Benchmarks. |
| Record Nr. | UNINA-9910747591203321 |
|
Xue Zhiyun
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Medical image learning with limited and noisy data : first international workshop, MILLanD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings / / edited by Ghada Zamzmi [and five others]
| Medical image learning with limited and noisy data : first international workshop, MILLanD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings / / edited by Ghada Zamzmi [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (243 pages) |
| Disciplina | 733 |
| Collana | Lecture Notes in Computer Science Ser. |
| Soggetto topico | Deep learning (Machine learning) |
| ISBN | 3-031-16760-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Efficient and Robust Annotation Strategies -- Heatmap Regression for Lesion Detection Using Pointwise Annotations -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Training via Heatmap Regression -- 3.2 Detection During Inference -- 3.3 Segmentation Transfer Learning -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Lesion Detection Results -- 4.3 Lesion Segmentation via Transfer Learning -- 5 Discussion and Conclusion -- References -- Partial Annotations for the Segmentation of Large Structures with Low Annotation Cost -- 1 Introduction -- 2 Method -- 2.1 Selective Dice Loss -- 2.2 Optimization -- 3 Experimental Results -- 4 Conclusion -- References -- Abstraction in Pixel-wise Noisy Annotations Can Guide Attention to Improve Prostate Cancer Grade Assessment -- 1 Introduction -- 2 Materials and Method -- 2.1 Data -- 2.2 Architecture -- 2.3 Multiple Instance Learning for Cancer Grade Assessment -- 2.4 Noisy Labels and Weak Supervision -- 3 Experiments -- 3.1 Implementation and Evaluation -- 3.2 Results -- 4 Conclusion -- References -- Meta Pixel Loss Correction for Medical Image Segmentation with Noisy Labels -- 1 Introduction -- 2 Methodology -- 2.1 Meta Pixel Loss Correction -- 2.2 Optimization Algorithm -- 3 Experiment Results -- 3.1 Dataset -- 3.2 Experiment Setting -- 3.3 Experimental Results -- 3.4 Limitation -- 4 Conclusion -- References -- Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction -- 1 Introduction -- 2 Materials -- 3 Study Design -- 4 Methods -- 4.1 Label Induction Using Machine Annotator -- 4.2 Similar Nodule Retrieval Using Metric Learning -- 5 Experiments and Results -- 5.1 Implementation -- 5.2 Quantitative Evaluation -- 5.3 Discussion -- 6 Conclusion and Future Work -- References.
Weakly-Supervised, Self-supervised, and Contrastive Learning -- Universal Lesion Detection and Classification Using Limited Data and Weakly-Supervised Self-training -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- BoxShrink: From Bounding Boxes to Segmentation Masks -- 1 Introduction -- 2 Related Work -- 3 Boxshrink Framework -- 3.1 Main Components -- 3.2 rapid-BoxShrink -- 3.3 robust-BoxShrink -- 4 Experiments -- 4.1 Qualitative and Quantitative Experiments -- 4.2 Reproducibility Details -- 5 Discussion -- 6 Conclusion -- References -- Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Conclusion -- References -- SB-SSL: Slice-Based Self-supervised Transformers for Knee Abnormality Classification from MRI -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Vision Transformer -- 3.2 Self-supervised Pretraining -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Results -- 4.3 Ablation Studies -- 5 Conclusion -- References -- Optimizing Transformations for Contrastive Learning in a Differentiable Framework -- 1 Introduction -- 2 Transformation Network -- 2.1 Optimizing Transformations -- 2.2 Differentiable Formulation of the Transformations -- 2.3 Experimental Settings -- 2.4 Linear Evaluation -- 3 Results and Discussion -- 4 Conclusions and Perspectives -- References -- Stain Based Contrastive Co-training for Histopathological Image Analysis -- 1 Introduction -- 2 Stain Based Contrastive Co-training -- 2.1 Stain Separation -- 2.2 Contrastive Co-training -- 3 Experiments -- 3.1 Datasets -- 3.2 Model Selection, Training and Hyperparameters -- 3.3 Results -- 3.4 Co-training View Analysis -- 3.5 Ablation Studies -- 4 Conclusion -- References. Active and Continual Learning -- .26em plus .1em minus .1emCLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification -- 1 Introduction -- 1.1 Related Work -- 1.2 Our Contributions -- 2 Preliminaries -- 2.1 Examples of Smi Functions -- 3 Clinical: Our Targeted Active Learning Framework for Binary and Long-Tail Imbalance -- 4 Experiments -- 4.1 Binary Imbalance -- 4.2 Long-Tail Imbalance -- 5 Conclusion -- References -- Real Time Data Augmentation Using Fractional Linear Transformations in Continual Learning -- 1 Introduction -- 2 Methodology -- 3 Experiments, Results and Discussion -- 4 Conclusion -- References -- DIAGNOSE: Avoiding Out-of-Distribution Data Using Submodular Information Measures -- 1 Introduction -- 1.1 Problem Statement: OOD Scenarios in Medical Data -- 1.2 Related Work -- 1.3 Our Contributions -- 2 Preliminaries -- 3 Leveraging Submodular Information Measures for Multiple Out-of-Distribution Scenarios -- 4 Experimental Results -- 4.1 Scenario A - Unrelated Images -- 4.2 Scenario B - Incorrectly Acquired Images -- 4.3 Scenario C - Mixed View Images -- 5 Conclusion -- References -- Transfer Representation Learning -- Auto-segmentation of Hip Joints Using MultiPlanar UNet with Transfer Learning -- 1 Introduction -- 2 Method -- 3 Data and Experiments -- 4 Results -- 4.1 Numerical Validation and Ablation Study -- 5 Conclusion -- References -- Asymmetry and Architectural Distortion Detection with Limited Mammography Data -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experiment Design -- 5 Experimental Results -- 5.1 Comparison with Other Methods -- 5.2 Ablation Study -- 6 Conclusions -- References -- Imbalanced Data and Out-of-Distribution Generalization -- Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT -- 1 Introduction -- 2 Methods -- 3 Experiments. 4 Results and Discussion -- 5 Conclusion -- References -- CVAD: An Anomaly Detector for Medical Images Based on Cascade VAE -- 1 Introduction -- 2 Method -- 2.1 CVAD Architecture -- 2.2 Combined Loss Function -- 2.3 Network Details -- 3 Experiments -- 3.1 Datasets and Implementation Details -- 3.2 Results -- 4 Conclusion -- References -- Approaches for Noisy, Missing, and Low Quality Data -- Visual Field Prediction with Missing and Noisy Data Based on Distance-Based Loss -- 1 Introduction -- 2 Method -- 2.1 Distance-Based Loss -- 3 Experiments -- 3.1 Dataset and Implementation -- 3.2 Results -- 4 Conclusion -- References -- Image Quality Classification for Automated Visual Evaluation of Cervical Precancer -- 1 Introduction -- 2 Image Quality Labeling Criteria and Data -- 2.1 The Labeling Criteria -- 2.2 Datasets -- 3 Methods -- 3.1 Cervix Detection -- 3.2 Quality Classification -- 3.3 Mislabel Identification -- 4 Experimental Results and Discussion -- 5 Conclusions -- Appendix -- References -- A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experiments -- 4.1 Models' Scalp Attention Pattern -- 4.2 Models' Sensitivity of Prediction on Inputs' Frequency -- 4.3 Model Sensitivity on Morphisms Between Samples -- 5 Conclusion -- References -- Automated Skin Biopsy Analysis with Limited Data -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Nerve Labeling -- 2.3 Dermis-Epidermis Boundary Detection -- 2.4 Nerve Crossing Identification -- 3 Experimental Setup -- 3.1 Evaluating the Nerve Tracing Model -- 3.2 Evaluating Dermis Model -- 4 Results -- 4.1 Nerve Labeling Results -- 4.2 Dermis Labeling Results -- 4.3 Crossing Count Results -- 5 Discussion -- References -- Author Index. |
| Record Nr. | UNISA-996490353303316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Medical Image Learning with Limited and Noisy Data : First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / / edited by Ghada Zamzmi, Sameer Antani, Ulas Bagci, Marius George Linguraru, Sivaramakrishnan Rajaraman, Zhiyun Xue
| Medical Image Learning with Limited and Noisy Data : First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / / edited by Ghada Zamzmi, Sameer Antani, Ulas Bagci, Marius George Linguraru, Sivaramakrishnan Rajaraman, Zhiyun Xue |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (243 pages) |
| Disciplina |
733
006.31 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-16760-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Efficient and Robust Annotation Strategies -- Heatmap Regression for Lesion Detection using Pointwise Annotations.- -- Partial Annotations for the Segmentation of Large Structures with Low Annotation.- -- Abstraction in Pixel-wise Noisy Annotations Can Guide Attention to Improve Prostate Cancer Grade Assessment -- Meta Pixel Loss Correction for Medical Image Segmentation with Noisy Labels -- Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction -- Weakly-supervised, Self-supervised, and Contrastive Learning -- Universal Lesion Detection and Classification using Limited Data and Weakly-Supervised Self-Training -- BoxShrink: From Bounding Boxes to Segmentation Masks -- Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis -- SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRI -- Optimizing Transformations for Contrastive Learning in a Differentiable Framework -- Stain-based Contrastive Co-training for Histopathological Image Analysis -- Active and Continual Learning -- CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification -- Real-time Data Augmentation using Fractional Linear Transformations in Continual Learning -- DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures -- Transfer Representation Learning -- Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning -- Asymmetry and Architectural Distortion Detection with Limited Mammography Data -- Imbalanced Data and Out-of-distribution Generalization -- Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT -- CVAD: An Anomaly Detector for Medical Images Based on Cascade -- Approaches for Noisy, Missing, and Low Quality Data -- Visual Field Prediction with Missing and Noisy Data Based on Distance-based Loss -- Image Quality Classification for Automated Visual Evaluation of Cervical Precancer -- A Monotonicity Constraint Attention Module for Emotion Classification with Limited EEG Data -- Automated Skin Biopsy Analysis with Limited Data. |
| Record Nr. | UNINA-9910595052403321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Predictive Intelligence in Medicine [[electronic resource] ] : 6th International Workshop, PRIME 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas, Ghada Zamzmi
| Predictive Intelligence in Medicine [[electronic resource] ] : 6th International Workshop, PRIME 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas, Ghada Zamzmi |
| Autore | Rekik Islem |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (306 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
AdeliEhsan
ParkSang Hyun CintasCelia ZamzmiGhada |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Artificial intelligence
Artificial Intelligence |
| ISBN | 3-031-46005-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996558470203316 |
|
Rekik Islem
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Predictive Intelligence in Medicine : 6th International Workshop, PRIME 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas, Ghada Zamzmi
| Predictive Intelligence in Medicine : 6th International Workshop, PRIME 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas, Ghada Zamzmi |
| Autore | Rekik Islem |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (306 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
AdeliEhsan
ParkSang Hyun CintasCelia ZamzmiGhada |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Artificial intelligence
Artificial Intelligence |
| ISBN | 3-031-46005-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910747595203321 |
|
Rekik Islem
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||