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.
Описание: 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.
Описание: This book constitutes the refereed joint proceedings of the 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the Third International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 9 full papers presented at CVII-STENT 2017 and the 12 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing.
This book constitutes the refereed joint proceedings of the 6th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017, and the Second International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017.
The 6 full papers presented at CVII-STENT 2017 and the 11 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing.
Автор: Agata Savary,Maciej Ogrodniczuk,Magdalena Zawisla Название: Coreference: Annotation, Resolution and Evaluation in Polish ISBN: 1614518351 ISBN-13(EAN): 9781614518358 Издательство: Walter de Gruyter Цена: 17656.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: ‘Coreference’ presents specificities of reference, anaphora and coreference in Polish, establish identity-of-reference annotation model and present methodology used to create the corpus of Polish general nominal coreference. Various resolution approaches are presented, followed by their evaluation. By discussing the subsequent steps of building a coreference-related component of the natural language processing toolset and offering deeper explanation of the decisions taken, this volume might also serve as a reference book on state-of the art methods of carrying out coreference projects for new languages and a tutorial for NLP practitioners.Apart from serving as a description of the fi rst complete approach to annotation and resolution of direct nominal coreference for Polish, this book is a useful starting point for further work on other types of anaphora/coreference, semantic annotation, cognitive linguistics (related to the topic of near-identity, discussed in the book) etc. With extended tutorial-like sections on important subtopics, such as evaluation metrics for coreference resolution, it can prove useful to both researchers and practitioners interested in semantic description of Balto-Slavic languages and their processing, engineers developing language resources, tools and linguistic processing chains, as well as computational linguists in general.
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