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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

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Table of Contents

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    Book Overview
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    Chapter 1 Semi-supervised Histological Image Segmentation via Hierarchical Consistency Enforcement
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    Chapter 2 Federated Stain Normalization for Computational Pathology
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    Chapter 3 DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification
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    Chapter 4 ReMix: A General and Efficient Framework for Multiple Instance Learning Based Whole Slide Image Classification
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    Chapter 5 S $$^3$$ 3 R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification
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    Chapter 6 Distilling Knowledge from Topological Representations for Pathological Complete Response Prediction
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    Chapter 7 SETMIL: Spatial Encoding Transformer-Based Multiple Instance Learning for Pathological Image Analysis
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    Chapter 8 Clinical-Realistic Annotation for Histopathology Images with Probabilistic Semi-supervision: A Worst-Case Study
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    Chapter 9 End-to-End Learning for Image-Based Detection of Molecular Alterations in Digital Pathology
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    Chapter 10 S5CL: Unifying Fully-Supervised, Self-supervised, and Semi-supervised Learning Through Hierarchical Contrastive Learning
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    Chapter 11 Sample Hardness Based Gradient Loss for Long-Tailed Cervical Cell Detection
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    Chapter 12 Test-Time Image-to-Image Translation Ensembling Improves Out-of-Distribution Generalization in Histopathology
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    Chapter 13 Predicting Molecular Traits from Tissue Morphology Through Self-interactive Multi-instance Learning
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    Chapter 14 InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation
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    Chapter 15 Improved Domain Generalization for Cell Detection in Histopathology Images via Test-Time Stain Augmentation
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    Chapter 16 Transformer Based Multiple Instance Learning for Weakly Supervised Histopathology Image Segmentation
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    Chapter 17 GradMix for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets
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    Chapter 18 Spatial-Hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification
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    Chapter 19 Gigapixel Whole-Slide Images Classification using Locally Supervised Learning
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    Chapter 20 Whole Slide Cervical Cancer Screening Using Graph Attention Network and Supervised Contrastive Learning
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    Chapter 21 RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization
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    Chapter 22 Identify Consistent Imaging Genomic Biomarkers for Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors
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    Chapter 23 Semi-supervised PR Virtual Staining for Breast Histopathological Images
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    Chapter 24 Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology
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    Chapter 25 Weakly Supervised Segmentation by Tensor Graph Learning for Whole Slide Images
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    Chapter 26 Test Time Transform Prediction for Open Set Histopathological Image Recognition
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    Chapter 27 Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis
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    Chapter 28 Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification
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    Chapter 29 Joint Region-Attention and Multi-scale Transformer for Microsatellite Instability Detection from Whole Slide Images in Gastrointestinal Cancer
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    Chapter 30 Self-supervised Pre-training for Nuclei Segmentation
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    Chapter 31 LifeLonger: A Benchmark for Continual Disease Classification
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    Chapter 32 Unsupervised Nuclei Segmentation Using Spatial Organization Priors
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    Chapter 33 Visual Deep Learning-Based Explanation for Neuritic Plaques Segmentation in Alzheimer’s Disease Using Weakly Annotated Whole Slide Histopathological Images
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    Chapter 34 MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation
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    Chapter 35 Region-Guided CycleGANs for Stain Transfer in Whole Slide Images
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    Chapter 36 Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology Image Classification
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    Chapter 37 Local Attention Graph-Based Transformer for Multi-target Genetic Alteration Prediction
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    Chapter 38 Incorporating Intratumoral Heterogeneity into Weakly-Supervised Deep Learning Models via Variance Pooling
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    Chapter 39 Prostate Cancer Histology Synthesis Using StyleGAN Latent Space Annotation
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    Chapter 40 Fast FF-to-FFPE Whole Slide Image Translation via Laplacian Pyramid and Contrastive Learning
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    Chapter 41 Feature Re-calibration Based Multiple Instance Learning for Whole Slide Image Classification
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    Chapter 42 Physiological Model Based Deep Learning Framework for Cardiac TMP Recovery
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    Chapter 43 DentalPointNet: Landmark Localization on High-Resolution 3D Digital Dental Models
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    Chapter 44 Landmark-Free Statistical Shape Modeling Via Neural Flow Deformations
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    Chapter 45 Learning Shape Distributions from Large Databases of Healthy Organs: Applications to Zero-Shot and Few-Shot Abnormal Pancreas Detection
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    Chapter 46 From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach
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    Chapter 47 Structure-Consistent Restoration Network for Cataract Fundus Image Enhancement
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    Chapter 48 Unsupervised Domain Adaptive Fundus Image Segmentation with Category-Level Regularization
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    Chapter 49 Degradation-Invariant Enhancement of Fundus Images via Pyramid Constraint Network
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    Chapter 50 A Spatiotemporal Model for Precise and Efficient Fully-Automatic 3D Motion Correction in OCT
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    Chapter 51 DA-Net: Dual Branch Transformer and Adaptive Strip Upsampling for Retinal Vessels Segmentation
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    Chapter 52 Visual Explanations for the Detection of Diabetic Retinopathy from Retinal Fundus Images
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    Chapter 53 Multidimensional Hypergraph on Delineated Retinal Features for Pathological Myopia Task
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    Chapter 54 Unsupervised Lesion-Aware Transfer Learning for Diabetic Retinopathy Grading in Ultra-Wide-Field Fundus Photography
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    Chapter 55 Local-Region and Cross-Dataset Contrastive Learning for Retinal Vessel Segmentation
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    Chapter 56 Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation
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    Chapter 57 Camera Adaptation for Fundus-Image-Based CVD Risk Estimation
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    Chapter 58 Opinions Vary? Diagnosis First!
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    Chapter 59 Learning Self-calibrated Optic Disc and Cup Segmentation from Multi-rater Annotations
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    Chapter 60 TINC: Temporally Informed Non-contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes
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    Chapter 61 DRGen: Domain Generalization in Diabetic Retinopathy Classification
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    Chapter 62 Frequency-Aware Inverse-Consistent Deep Learning for OCT-Angiogram Super-Resolution
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    Chapter 63 A Multi-task Network with Weight Decay Skip Connection Training for Anomaly Detection in Retinal Fundus Images
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    Chapter 64 Multiscale Unsupervised Retinal Edema Area Segmentation in OCT Images
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    Chapter 65 SeATrans: Learning Segmentation-Assisted Diagnosis Model via Transformer
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    Chapter 66 Screening of Dementia on OCTA Images via Multi-projection Consistency and Complementarity
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    Chapter 67 Noise Transfer for Unsupervised Domain Adaptation of Retinal OCT Images
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    Chapter 68 Long-Tailed Multi-label Retinal Diseases Recognition via Relational Learning and Knowledge Distillation
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    Chapter 69 Weakly Supervised Online Action Detection for Infant General Movements
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    Chapter 70 Super-Focus: Domain Adaptation for Embryo Imaging via Self-supervised Focal Plane Regression
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    Chapter 71 SUPER-IVIM-DC: Intra-voxel Incoherent Motion Based Fetal Lung Maturity Assessment from Limited DWI Data Using Supervised Learning Coupled with Data-Consistency
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    Chapter 72 Automated Classification of General Movements in Infants Using Two-Stream Spatiotemporal Fusion Network
Attention for Chapter 13: Predicting Molecular Traits from Tissue Morphology Through Self-interactive Multi-instance Learning
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Chapter title
Predicting Molecular Traits from Tissue Morphology Through Self-interactive Multi-instance Learning
Chapter number 13
Book title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-16434-7_13
Book ISBNs
978-3-03-116433-0, 978-3-03-116434-7
Authors

Hu, Yang, Sirinukunwattana, Korsuk, Gaitskell, Kezia, Wood, Ruby, Verrill, Clare, Rittscher, Jens

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 20%
Unspecified 1 10%
Student > Postgraduate 1 10%
Other 1 10%
Unknown 5 50%
Readers by discipline Count As %
Engineering 2 20%
Unspecified 1 10%
Agricultural and Biological Sciences 1 10%
Computer Science 1 10%
Unknown 5 50%