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Medical Image Learning with Limited and Noisy Data

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Cover of 'Medical Image Learning with Limited and Noisy Data'

Table of Contents

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    Book Overview
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    Chapter 1 Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations
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    Chapter 2 ScribSD: Scribble-Supervised Fetal MRI Segmentation Based on Simultaneous Feature and Prediction Self-distillation
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    Chapter 3 Label-Efficient Contrastive Learning-Based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images
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    Chapter 4 Affordable Graph Neural Network Framework Using Topological Graph Contraction
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    Chapter 5 Dual-Domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-Angle Reconstruction of Low-Dose Cardiac SPECT
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    Chapter 6 A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging
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    Chapter 7 COVID-19 Lesion Segmentation Framework for the Contrast-Enhanced CT in the Absence of Contrast-Enhanced CT Annotations
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    Chapter 8 Feasibility of Universal Anomaly Detection Without Knowing the Abnormality in Medical Images
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    Chapter 9 Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection
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    Chapter 10 FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation
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    Chapter 11 Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy
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    Chapter 12 Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning
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    Chapter 13 SDLFormer: A Sparse and Dense Locality-Enhanced Transformer for Accelerated MR Image Reconstruction
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    Chapter 14 Robust Unsupervised Image to Template Registration Without Image Similarity Loss
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    Chapter 15 A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue
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    Chapter 16 Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification
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    Chapter 17 Exigent Examiner and Mean Teacher: An Advanced 3D CNN-Based Semi-Supervised Brain Tumor Segmentation Framework
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    Chapter 18 Extremely Weakly-Supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation
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    Chapter 19 Multi-task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Images
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    Chapter 21 Test-Time Augmentation-Based Active Learning and Self-training for Label-Efficient Segmentation
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    Chapter 22 Active Transfer Learning for 3D Hippocampus Segmentation
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    Chapter 23 Using Training Samples as Transitive Information Bridges in Predicted 4D MRI
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    Chapter 24 To Pretrain or Not to Pretrain? A Case Study of Domain-Specific Pretraining for Semantic Segmentation in Histopathology
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    Chapter 25 Large-Scale Pretraining on Pathological Images for Fine-Tuning of Small Pathological Benchmarks
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Title
Medical Image Learning with Limited and Noisy Data
Published by
Springer Nature Switzerland, November 2023
DOI 10.1007/978-3-031-44917-8
ISBNs
978-3-03-147196-4, 978-3-03-144917-8
Editors

Xue, Zhiyun, Antani, Sameer, Zamzmi, Ghada, Yang, Feng, Rajaraman, Sivaramakrishnan, Huang, Sharon Xiaolei, Linguraru, Marius George, Liang, Zhaohui

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