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Interpretable and annotation-efficient learning for medical image computing : third international workshop, iMIMIC 2020, second international workshop, MIL3ID 2020, and 5th international workshop, LABELS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings / / Jaime Cardoso, Hien Van Nguyen, Nicholas Heller
| Interpretable and annotation-efficient learning for medical image computing : third international workshop, iMIMIC 2020, second international workshop, MIL3ID 2020, and 5th international workshop, LABELS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings / / Jaime Cardoso, Hien Van Nguyen, Nicholas Heller |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XVII, 292 p. 109 illus.) |
| Disciplina | 616.0757 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Image Processing and Computer Vision Computer Appl. in Social and Behavioral Sciences |
| ISBN | 3-030-61166-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | iMIMIC 2020 -- Assessing attribution maps for explaining CNN-based vertebral fracture classifiers -- Projective Latent Interventions for Understanding and Fine-tuning Classifiers -- Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging -- Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations -- Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations -- Explainable Disease Classification via weakly-supervised segmentation -- Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns -- Explainability for regression CNN in fetal head circumference estimation from ultrasound images -- MIL3ID 2020 -- Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins -- Semi-supervised Instance Segmentation with a Learned Shape Prior -- COMe-SEE: Cross-Modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs -- Semi-supervised Machine Learning with MixMatch and Equivalence Classes -- Non-contrast CT Liver Segmentation using CycleGAN Data Augmentation from Contrast Enhanced CT -- Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation -- A Case Study of Transfer of Lesion-Knowledge -- Transfer Learning With Joint Optimization for Label-Efficient Medical Image Anomaly Detection -- Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation -- HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification -- Semi-supervised classification of chest radiographs -- LABELS 2020 -- Risk of training diagnostic algorithms on data with demographic bias -- Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks -- Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels -- EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology -- Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection -- Labeling of Multilingual Breast MRI Reports -- Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning -- Labelling imaging datasets on the basis of neuroradiology reports: a validation study -- Semi-Supervised Learning for Instrument Detection with a Class Imbalanced Dataset -- Paying Per-label Attention for Multi-label Extraction from Radiology Reports. |
| Record Nr. | UNINA-9910427709103321 |
| Cham, Switzerland : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Interpretable and annotation-efficient learning for medical image computing : third international workshop, iMIMIC 2020, second international workshop, MIL3ID 2020, and 5th international workshop, LABELS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings / / Jaime Cardoso, Hien Van Nguyen, Nicholas Heller
| Interpretable and annotation-efficient learning for medical image computing : third international workshop, iMIMIC 2020, second international workshop, MIL3ID 2020, and 5th international workshop, LABELS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings / / Jaime Cardoso, Hien Van Nguyen, Nicholas Heller |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XVII, 292 p. 109 illus.) |
| Disciplina | 616.0757 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Image Processing and Computer Vision Computer Appl. in Social and Behavioral Sciences |
| ISBN | 3-030-61166-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | iMIMIC 2020 -- Assessing attribution maps for explaining CNN-based vertebral fracture classifiers -- Projective Latent Interventions for Understanding and Fine-tuning Classifiers -- Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging -- Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations -- Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations -- Explainable Disease Classification via weakly-supervised segmentation -- Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns -- Explainability for regression CNN in fetal head circumference estimation from ultrasound images -- MIL3ID 2020 -- Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins -- Semi-supervised Instance Segmentation with a Learned Shape Prior -- COMe-SEE: Cross-Modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs -- Semi-supervised Machine Learning with MixMatch and Equivalence Classes -- Non-contrast CT Liver Segmentation using CycleGAN Data Augmentation from Contrast Enhanced CT -- Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation -- A Case Study of Transfer of Lesion-Knowledge -- Transfer Learning With Joint Optimization for Label-Efficient Medical Image Anomaly Detection -- Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation -- HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification -- Semi-supervised classification of chest radiographs -- LABELS 2020 -- Risk of training diagnostic algorithms on data with demographic bias -- Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks -- Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels -- EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology -- Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection -- Labeling of Multilingual Breast MRI Reports -- Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning -- Labelling imaging datasets on the basis of neuroradiology reports: a validation study -- Semi-Supervised Learning for Instrument Detection with a Class Imbalanced Dataset -- Paying Per-label Attention for Multi-label Extraction from Radiology Reports. |
| Record Nr. | UNISA-996418301503316 |
| Cham, Switzerland : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention [[electronic resource] ] : International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Luping Zhou, Nicholas Heller, Yiyu Shi, Yiming Xiao, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, X. Sharon Hu, Danny Chen, Matthieu Chabanas, Hassan Rivaz, Ingerid Reinertsen
| Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention [[electronic resource] ] : International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Luping Zhou, Nicholas Heller, Yiyu Shi, Yiming Xiao, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, X. Sharon Hu, Danny Chen, Matthieu Chabanas, Hassan Rivaz, Ingerid Reinertsen |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XX, 154 p. 62 illus., 48 illus. in color.) |
| Disciplina |
006.6
006.37 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Health informatics Image Processing and Computer Vision Artificial Intelligence Health Informatics |
| ISBN | 3-030-33642-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS 2019) -- Comparison of active learning strategies applied to lung nodule segmentation in CT scans -- Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotation -- XiangyaDerm: A Clinical Image Dataset of Asian Race for Skin Disease Aided Diagnosis -- Data Augmentation based on Substituting Regional MRI Volume Scores -- Weakly supervised segmentation from extreme points -- Exploring the Relationship between Segmentation Uncertainty, Segmentation Performance and Inter-observer Variability with Probabilistic Networks -- DeepIGeoS-V2: Deep Interactive Segmentation of Multiple Organs from Head and Neck Images with Lightweight CNNs -- The Role of Publicly Available Data in MICCAI Papers from 2014 to 2018 -- First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019) -- Hardware Acceleration of Persistent Homology Computation -- Deep Compressed Pneumonia Detection for Low-Power Embedded Devices -- D3MC: A Reinforcement Learning based Data-driven Dyna Model Compression -- An Analytical Method of Automatic Alignment for Electron Tomography -- Fixed-Point U-Net Quantization for Medical Image Segmentation -- Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2019) -- Registration of ultrasound volumes based on Euclidean distance transform -- Landmark-based evaluation of a block-matching registration framework on the RESECT pre- and intra-operative brain image data set -- Comparing deep learning strategies and attention mechanisms of discrete registration for multimodal image-guided interventions. |
| Record Nr. | UNINA-9910357847003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention [[electronic resource] ] : International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Luping Zhou, Nicholas Heller, Yiyu Shi, Yiming Xiao, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, X. Sharon Hu, Danny Chen, Matthieu Chabanas, Hassan Rivaz, Ingerid Reinertsen
| Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention [[electronic resource] ] : International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Luping Zhou, Nicholas Heller, Yiyu Shi, Yiming Xiao, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, X. Sharon Hu, Danny Chen, Matthieu Chabanas, Hassan Rivaz, Ingerid Reinertsen |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XX, 154 p. 62 illus., 48 illus. in color.) |
| Disciplina |
006.6
006.37 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Health informatics Image Processing and Computer Vision Artificial Intelligence Health Informatics |
| ISBN | 3-030-33642-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS 2019) -- Comparison of active learning strategies applied to lung nodule segmentation in CT scans -- Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotation -- XiangyaDerm: A Clinical Image Dataset of Asian Race for Skin Disease Aided Diagnosis -- Data Augmentation based on Substituting Regional MRI Volume Scores -- Weakly supervised segmentation from extreme points -- Exploring the Relationship between Segmentation Uncertainty, Segmentation Performance and Inter-observer Variability with Probabilistic Networks -- DeepIGeoS-V2: Deep Interactive Segmentation of Multiple Organs from Head and Neck Images with Lightweight CNNs -- The Role of Publicly Available Data in MICCAI Papers from 2014 to 2018 -- First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019) -- Hardware Acceleration of Persistent Homology Computation -- Deep Compressed Pneumonia Detection for Low-Power Embedded Devices -- D3MC: A Reinforcement Learning based Data-driven Dyna Model Compression -- An Analytical Method of Automatic Alignment for Electron Tomography -- Fixed-Point U-Net Quantization for Medical Image Segmentation -- Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2019) -- Registration of ultrasound volumes based on Euclidean distance transform -- Landmark-based evaluation of a block-matching registration framework on the RESECT pre- and intra-operative brain image data set -- Comparing deep learning strategies and attention mechanisms of discrete registration for multimodal image-guided interventions. |
| Record Nr. | UNISA-996466295103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||