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Interpretable and Annotation-Efficient Learning for Medical Image Computing

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Cover of 'Interpretable and Annotation-Efficient Learning for Medical Image Computing'

Table of Contents

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    Book Overview
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    Chapter 1 Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers
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    Chapter 2 Projective Latent Interventions for Understanding and Fine-Tuning Classifiers
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    Chapter 3 Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging
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    Chapter 4 Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations
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    Chapter 5 Improving Interpretability for Computer-Aided Diagnosis Tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-Based Explanations
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    Chapter 6 Explainable Disease Classification via Weakly-Supervised Segmentation
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    Chapter 7 Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns
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    Chapter 8 Explainability for Regression CNN in Fetal Head Circumference Estimation from Ultrasound Images
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    Chapter 9 Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins
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    Chapter 10 Semi-supervised Instance Segmentation with a Learned Shape Prior
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    Chapter 11 COMe-SEE: Cross-modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs
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    Chapter 12 Semi-supervised Machine Learning with MixMatch and Equivalence Classes
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    Chapter 13 Non-contrast CT Liver Segmentation Using CycleGAN Data Augmentation from Contrast Enhanced CT
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    Chapter 14 Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation
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    Chapter 15 A Case Study of Transfer of Lesion-Knowledge
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    Chapter 16 Transfer Learning with Joint Optimization for Label-Efficient Medical Image Anomaly Detection
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    Chapter 17 Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-domain Liver Segmentation
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    Chapter 18 HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification
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    Chapter 19 Semi-supervised Classification of Chest Radiographs
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    Chapter 20 Risk of Training Diagnostic Algorithms on Data with Demographic Bias
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    Chapter 21 Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks
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    Chapter 22 Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels
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    Chapter 23 EasierPath: An Open-Source Tool for Human-in-the-Loop Deep Learning of Renal Pathology
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    Chapter 24 Imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection
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    Chapter 25 Labeling of Multilingual Breast MRI Reports
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    Chapter 26 Predicting Scores of Medical Imaging Segmentation Methods with Meta-learning
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    Chapter 27 Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study
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    Chapter 28 Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset
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    Chapter 29 Paying Per-Label Attention for Multi-label Extraction from Radiology Reports
Attention for Chapter 11: COMe-SEE: Cross-modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs
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Chapter title
COMe-SEE: Cross-modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs
Chapter number 11
Book title
Interpretable and Annotation-Efficient Learning for Medical Image Computing
Published by
Springer, Cham, October 2020
DOI 10.1007/978-3-030-61166-8_11
Book ISBNs
978-3-03-061165-1, 978-3-03-061166-8
Authors

Angshuman Paul, Thomas C. Shen, Niranjan Balachandar, Yuxing Tang, Yifan Peng, Zhiyong Lu, Ronald M. Summers

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Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 100%
Researcher 1 100%
Readers by discipline Count As %
Unspecified 1 100%
Computer Science 1 100%