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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 | ||
| ||
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis [[electronic resource] ] : First International Workshop, OR 2.0 2018, 5th International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, Third International Workshop, ISIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings / / edited by Danail Stoyanov, Zeike Taylor, Duygu Sarikaya, Jonathan McLeod, Miguel Angel González Ballester, Noel C.F. Codella, Anne Martel, Lena Maier-Hein, Anand Malpani, Marco A. Zenati, Sandrine De Ribaupierre, Luo Xiongbiao, Toby Collins, Tobias Reichl, Klaus Drechsler, Marius Erdt, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, M. Emre Celebi, Kristin Dana, Allan Halpern
| OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis [[electronic resource] ] : First International Workshop, OR 2.0 2018, 5th International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, Third International Workshop, ISIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings / / edited by Danail Stoyanov, Zeike Taylor, Duygu Sarikaya, Jonathan McLeod, Miguel Angel González Ballester, Noel C.F. Codella, Anne Martel, Lena Maier-Hein, Anand Malpani, Marco A. Zenati, Sandrine De Ribaupierre, Luo Xiongbiao, Toby Collins, Tobias Reichl, Klaus Drechsler, Marius Erdt, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, M. Emre Celebi, Kristin Dana, Allan Halpern |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (xxii, 323 pages) : illustrations |
| Disciplina | 616.0754 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Computer vision Computer networks Computers, Special purpose Artificial Intelligence Computer Vision Computer Communication Networks Special Purpose and Application-Based Systems |
| ISBN | 3-030-01201-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | OR 2.0 Context-Aware Operating Theaters, OR 2.0 2018 -- Computer Assisted Robotic Endoscopy, CARE 2018 -- Clinical Image-Based Procedures, CLIP 2018 -- Skin Image Analysis, ISIC 2018. |
| Record Nr. | UNINA-9910349400003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis [[electronic resource] ] : First International Workshop, OR 2.0 2018, 5th International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, Third International Workshop, ISIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings / / edited by Danail Stoyanov, Zeike Taylor, Duygu Sarikaya, Jonathan McLeod, Miguel Angel González Ballester, Noel C.F. Codella, Anne Martel, Lena Maier-Hein, Anand Malpani, Marco A. Zenati, Sandrine De Ribaupierre, Luo Xiongbiao, Toby Collins, Tobias Reichl, Klaus Drechsler, Marius Erdt, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, M. Emre Celebi, Kristin Dana, Allan Halpern
| OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis [[electronic resource] ] : First International Workshop, OR 2.0 2018, 5th International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, Third International Workshop, ISIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings / / edited by Danail Stoyanov, Zeike Taylor, Duygu Sarikaya, Jonathan McLeod, Miguel Angel González Ballester, Noel C.F. Codella, Anne Martel, Lena Maier-Hein, Anand Malpani, Marco A. Zenati, Sandrine De Ribaupierre, Luo Xiongbiao, Toby Collins, Tobias Reichl, Klaus Drechsler, Marius Erdt, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, M. Emre Celebi, Kristin Dana, Allan Halpern |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (xxii, 323 pages) : illustrations |
| Disciplina | 616.0754 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Computer vision Computer networks Computers, Special purpose Artificial Intelligence Computer Vision Computer Communication Networks Special Purpose and Application-Based Systems |
| ISBN | 3-030-01201-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | OR 2.0 Context-Aware Operating Theaters, OR 2.0 2018 -- Computer Assisted Robotic Endoscopy, CARE 2018 -- Clinical Image-Based Procedures, CLIP 2018 -- Skin Image Analysis, ISIC 2018. |
| Record Nr. | UNISA-996466331503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||