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Medical Optical Imaging and Virtual Microscopy Image Analysis

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Cover of 'Medical Optical Imaging and Virtual Microscopy Image Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Cell Counting with Inverse Distance Kernel and Self-supervised Learning
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    Chapter 2 Predicting the Visual Attention of Pathologists Evaluating Whole Slide Images of Cancer
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    Chapter 3 Edge-Based Self-supervision for Semi-supervised Few-Shot Microscopy Image Cell Segmentation
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    Chapter 4 Joint Denoising and Super-Resolution for Fluorescence Microscopy Using Weakly-Supervised Deep Learning
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    Chapter 5 MxIF Q-score: Biology-Informed Quality Assurance for Multiplexed Immunofluorescence Imaging
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    Chapter 6 A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology
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    Chapter 7 Leukocyte Classification Using Multimodal Architecture Enhanced by Knowledge Distillation
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    Chapter 8 Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models
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    Chapter 9 Profiling DNA Damage in 3D Histology Samples
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    Chapter 10 Few-Shot Segmentation of Microscopy Images Using Gaussian Process
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    Chapter 11 Adversarial Stain Transfer to Study the Effect of Color Variation on Cell Instance Segmentation
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    Chapter 12 Constrained Self-supervised Method with Temporal Ensembling for Fiber Bundle Detection on Anatomic Tracing Data
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    Chapter 13 Sequential Multi-task Learning for Histopathology-Based Prediction of Genetic Mutations with Extremely Imbalanced Labels
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    Chapter 14 Morph-Net: End-to-End Prediction of Nuclear Morphological Features from Histology Images
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    Chapter 15 A Light-Weight Interpretable Model for Nuclei Detection and Weakly-Supervised Segmentation
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    Chapter 16 A Coarse-to-Fine Segmentation Methodology Based on Deep Networks for Automated Analysis of  Cryptosporidium Parasite from Fluorescence Microscopic Images
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    Chapter 17 Swin Faster R-CNN for Senescence Detection of Mesenchymal Stem Cells in Bright-Field Images
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    Chapter 18 Characterizing Continual Learning Scenarios for Tumor Classification in Histopathology Images
Attention for Chapter 8: Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models
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Chapter title
Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models
Chapter number 8
Book title
Medical Optical Imaging and Virtual Microscopy Image Analysis
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-16961-8_8
Book ISBNs
978-3-03-116960-1, 978-3-03-116961-8
Authors

Fischer, Maximilian, Neher, Peter, Götz, Michael, Xiao, Shuhan, Almeida, Silvia Dias, Schüffler, Peter, Muckenhuber, Alexander, Braren, Rickmer, Kleesiek, Jens, Nolden, Marco, Maier-Hein, Klaus

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