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Interpretable and Annotation-Efficient Learning for Medical Image Computing

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

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    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
  4. Altmetric Badge
    Chapter 3 Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging
  5. Altmetric Badge
    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
  7. Altmetric Badge
    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
  21. Altmetric Badge
    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
  30. Altmetric Badge
    Chapter 29 Paying Per-Label Attention for Multi-label Extraction from Radiology Reports
Attention for Chapter 9: Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins
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  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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Chapter title
Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins
Chapter number 9
Book title
Interpretable and Annotation-Efficient Learning for Medical Image Computing
Published in
arXiv, October 2020
DOI 10.1007/978-3-030-61166-8_9
Book ISBNs
978-3-03-061165-1, 978-3-03-061166-8
Authors

Özgün Çiçek, Yassine Marrakchi, Enoch Boasiako Antwi, Barbara Di Ventura, Thomas Brox, Çiçek, Özgün, Marrakchi, Yassine, Boasiako Antwi, Enoch, Di Ventura, Barbara, Brox, Thomas

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

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 %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 November 2020.
All research outputs
#15,175,585
of 24,093,053 outputs
Outputs from arXiv
#295,510
of 1,018,817 outputs
Outputs of similar age
#231,267
of 414,198 outputs
Outputs of similar age from arXiv
#9,601
of 33,340 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,018,817 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 414,198 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33,340 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.

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