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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 1: Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers
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Chapter title
Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers
Chapter number 1
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_1
Book ISBNs
978-3-03-061165-1, 978-3-03-061166-8
Authors

Eren Bora Yilmaz, Alexander Oliver Mader, Tobias Fricke, Jaime Peña, Claus-Christian Glüer, Carsten Meyer, Yilmaz, Eren Bora, Mader, Alexander Oliver, Fricke, Tobias, Peña, Jaime, Glüer, Claus-Christian, Meyer, Carsten

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 25%
Student > Master 2 17%
Student > Doctoral Student 2 17%
Student > Bachelor 1 8%
Unknown 4 33%
Readers by discipline Count As %
Computer Science 8 67%
Unknown 4 33%