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Cancer Prevention Through Early Detection

Overview of attention for book
Cancer Prevention Through Early Detection
Springer Nature Switzerland

Table of Contents

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    Book Overview
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    Chapter 1 A Deep Attention-Multiple Instance Learning Framework to Predict Survival of Soft-Tissue Sarcoma from Whole Slide Images
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    Chapter 2 Towards Real-Time Confirmation of Breast Cancer in the OR Using CNN-Based Raman Spectroscopy Classification
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    Chapter 3 Fully Automated CAD System for Lung Cancer Detection and Classification Using 3D Residual U-Net with multi-Region Proposal Network (mRPN) in CT Images
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    Chapter 4 Image Captioning for Automated Grading and Understanding of Ulcerative Colitis
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    Chapter 5 Multispectral 3D Masked Autoencoders for Anomaly Detection in Non-Contrast Enhanced Breast MRI
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    Chapter 6 Non-redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer
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    Chapter 7 Assessing the Performance of Deep Learning-Based Models for Prostate Cancer Segmentation Using Uncertainty Scores
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    Chapter 8 MoSID: Modality-Specific Information Disentanglement from Multi-parametric MRI for Breast Tumor Segmentation
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    Chapter 9 Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time
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    Chapter 10 ColNav: Real-Time Colon Navigation for Colonoscopy
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    Chapter 11 Modeling Barrett’s Esophagus Progression Using Geometric Variational Autoencoders
Attention for Chapter 2: Towards Real-Time Confirmation of Breast Cancer in the OR Using CNN-Based Raman Spectroscopy Classification
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mendeley
6 Mendeley
Summary
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Chapter title
Towards Real-Time Confirmation of Breast Cancer in the OR Using CNN-Based Raman Spectroscopy Classification
Chapter number 2
Book title
Cancer Prevention Through Early Detection
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-45350-2_2
Book ISBNs
978-3-03-145349-6, 978-3-03-145350-2
Authors

Grajales, David, Le, William, Dallaire, Frédérick, Sheehy, Guillaume, David, Sandryne, Tran, Trang, Leblond, Frédéric, Ménard, Cynthia, Kadoury, Samuel

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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 %
Unspecified 1 17%
Student > Bachelor 1 17%
Unknown 4 67%
Readers by discipline Count As %
Unspecified 1 17%
Unknown 5 83%

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