Cardoso Jaime, Van Nguyen Hien, Heller Nicholas Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, IMIMIC 2020, Second International Workshop, 9783030611651

Варианты приобретения

Цена: 46570.00T
Кол-во:
Наличие: Поставка под заказ. Есть в наличии на складе поставщика.
Склад Америка: 135 шт.
При оформлении заказа до: 2025-07-28
Ориентировочная дата поставки: Август-начало Сентября
При условии наличия книги у поставщика.

Добавить в корзину

в Мои желания

Автор: Cardoso Jaime, Van Nguyen Hien, Heller Nicholas
Название: Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, IMIMIC 2020, Second International Workshop,
ISBN: 9783030611651
Издательство: Springer
Классификация:

Биология и науки о жизни

Приложения для деловой сферы

Теория вычислительных машин и систем, кибернетика

Искусственный интеллект

Распознавание образов

Машинное зрение

ISBN-10: 3030611655
Обложка/Формат: Paperback
Страницы: 292
Вес: 0.44 кг.
Дата издания: 23.11.2020
Серия: Image processing, computer vision, pattern recognition, and graphics
Язык: English
Издание: 1st ed. 2020
Иллюстрации: 109 illustrations, black and white; xvii, 292 p. 109 illus.; 109 illustrations, black and white; xvii, 292 p. 109 illus.
Размер: 23.39 x 15.60 x 1.70 cm
Читательская аудитория: Professional & vocational
Подзаголовок: 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
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
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.
Дополнительное 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

Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, IMIMIC 2020, Second International Workshop,, Cardoso Jaime, Van Nguyen Hien, Heller Nicholas