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  • Produktbild: Interpretable and Annotation-Efficient Learning for Medical Image Computing
  • Produktbild: Interpretable and Annotation-Efficient Learning for Medical Image Computing
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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

Aus der Reihe Image Processing, Computer Vision, Pattern Recognition, and Graphics

51,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.10.2020

Abbildungen

XVII, 292 p. 109 illus.

Herausgeber

Hien Van Nguyen + weitere

Verlag

Springer

Seitenzahl

292

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

482 g

Auflage

1st ed. 2020

Sprache

Englisch

ISBN

978-3-030-61165-1

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.10.2020

Abbildungen

XVII, 292 p. 109 illus.

Herausgeber

Verlag

Springer

Seitenzahl

292

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

482 g

Auflage

1st ed. 2020

Sprache

Englisch

ISBN

978-3-030-61165-1

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Interpretable and Annotation-Efficient Learning for Medical Image Computing
  • Produktbild: Interpretable and Annotation-Efficient Learning for Medical Image Computing
  • 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.