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Multiscale Multimodal Medical Imaging

Overview of attention for book
Cover of 'Multiscale Multimodal Medical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 $$\mathrm {M^{2}F}$$ M 2 F : A Multi-modal and Multi-task Fusion Network for Glioma Diagnosis and Prognosis
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    Chapter 2 Visual Modalities Based Multimodal Fusion for Surgical Phase Recognition
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    Chapter 3 Cross-Scale Attention Guided Multi-instance Learning for Crohn’s Disease Diagnosis with Pathological Images
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    Chapter 4 Vessel Segmentation via Link Prediction of Graph Neural Networks
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    Chapter 5 A Bagging Strategy-Based Multi-scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset
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    Chapter 6 Liver Segmentation Quality Control in Multi-sequence MR Studies
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    Chapter 7 Pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI
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    Chapter 8 Gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical Image Segmentation
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    Chapter 9 Learning-Based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study
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    Chapter 10 Coordinate Translator for Learning Deformable Medical Image Registration
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    Chapter 11 Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation
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    Chapter 12 Improved Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT Scans
Attention for Chapter 11: Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation
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Chapter title
Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation
Chapter number 11
Book title
Multiscale Multimodal Medical Imaging
Published in
arXiv, January 2022
DOI 10.1007/978-3-031-18814-5_11
Book ISBNs
978-3-03-118813-8, 978-3-03-118814-5
Authors

Mojtahedi, Ramtin, Hamghalam, Mohammad, Do, Richard K. G., Simpson, Amber L., Ramtin Mojtahedi, Mohammad Hamghalam, Richard K. G. Do, Amber L. Simpson

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 17%
Unknown 5 83%
Readers by discipline Count As %
Medicine and Dentistry 1 17%
Engineering 1 17%
Unknown 4 67%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 01 September 2023.
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#21,836,259
of 24,366,830 outputs
Outputs from arXiv
#767,956
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Outputs of similar age
#431,169
of 509,489 outputs
Outputs of similar age from arXiv
#22,386
of 33,062 outputs
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We're also able to compare this research output to 33,062 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.