Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

Table of Contents

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   Book Overview
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   Chapter 1 Relational Subsets Knowledge Distillation for Long-Tailed Retinal Diseases Recognition
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   Chapter 2 Cross-Domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography
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   Chapter 3 Distinguishing Differences Matters: Focal Contrastive Network for Peripheral Anterior Synechiae Recognition
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   Chapter 4 RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs Using a Novel Multi-scale Generative Adversarial Network
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   Chapter 5 MIL-VT: Multiple Instance Learning Enhanced Vision Transformer for Fundus Image Classification
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   Chapter 6 Local-Global Dual Perception Based Deep Multiple Instance Learning for Retinal Disease Classification
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   Chapter 7 BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images
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   Chapter 8 LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos
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    Chapter 9 I-SECRET: Importance-Guided Fundus Image Enhancement via Semi-supervised Contrastive Constraining
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    Chapter 10 Few-Shot Transfer Learning for Hereditary Retinal Diseases Recognition
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    Chapter 11 Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images
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    Chapter 12 GQ-GCN: Group Quadratic Graph Convolutional Network for Classification of Histopathological Images
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    Chapter 13 Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network
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    Chapter 14 Prototypical Models for Classifying High-Risk Atypical Breast Lesions
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    Chapter 15 Hierarchical Attention Guided Framework for Multi-resolution Collaborative Whole Slide Image Segmentation
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    Chapter 16 Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms
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    Chapter 17 A Computational Geometry Approach for Modeling Neuronal Fiber Pathways
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    Chapter 18 TransPath: Transformer-Based Self-supervised Learning for Histopathological Image Classification
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    Chapter 19 From Pixel to Whole Slide: Automatic Detection of Microvascular Invasion in Hepatocellular Carcinoma on Histopathological Image via Cascaded Networks
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    Chapter 20 DT-MIL: Deformable Transformer for Multi-instance Learning on Histopathological Image
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    Chapter 21 Early Detection of Liver Fibrosis Using Graph Convolutional Networks
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    Chapter 22 Hierarchical Graph Pathomic Network for Progression Free Survival Prediction
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    Chapter 23 Increasing Consistency of Evoked Response in Thalamic Nuclei During Repetitive Burst Stimulation of Peripheral Nerve in Humans
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    Chapter 24 Weakly Supervised Pan-Cancer Segmentation Tool
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    Chapter 25 Structure-Preserving Multi-domain Stain Color Augmentation Using Style-Transfer with Disentangled Representations
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    Chapter 26 MetaCon: Meta Contrastive Learning for Microsatellite Instability Detection
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    Chapter 27 Generalizing Nucleus Recognition Model in Multi-source Ki67 Immunohistochemistry Stained Images via Domain-Specific Pruning
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    Chapter 28 Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification
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    Chapter 29 Instance-Based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image
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    Chapter 30 Hybrid Supervision Learning for Pathology Whole Slide Image Classification
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    Chapter 31 MorphSet: Improving Renal Histopathology Case Assessment Through Learned Prognostic Vectors
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    Chapter 32 Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
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    Chapter 33 Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction Using Patch-Based Graph Convolutional Networks
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    Chapter 34 Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images
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    Chapter 35 Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field
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    Chapter 36 Semi-supervised Cell Detection in Time-Lapse Images Using Temporal Consistency
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    Chapter 37 Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap
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    Chapter 38 2D Histology Meets 3D Topology: Cytoarchitectonic Brain Mapping with Graph Neural Networks
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    Chapter 39 Annotation-Efficient Cell Counting
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    Chapter 40 A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos
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    Chapter 41 Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification
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    Chapter 42 Learning Neuron Stitching for Connectomics
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    Chapter 43 CA2.5-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection
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    Chapter 44 Automated Malaria Cells Detection from Blood Smears Under Severe Class Imbalance via Importance-Aware Balanced Group Softmax
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    Chapter 45 Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images
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    Chapter 46 Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy
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    Chapter 47 Deep Reinforcement Exemplar Learning for Annotation Refinement
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    Chapter 48 Instance-Aware Feature Alignment for Cross-Domain Cell Nuclei Detection in Histopathology Images
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    Chapter 49 Positive-Unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations
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    Chapter 50 GloFlow: Whole Slide Image Stitching from Video Using Optical Flow and Global Image Alignment
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    Chapter 51 Multi-modal Multi-instance Learning Using Weakly Correlated Histopathological Images and Tabular Clinical Information
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    Chapter 52 Ranking Loss: A Ranking-Based Deep Neural Network for Colorectal Cancer Grading in Pathology Images
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    Chapter 53 Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains
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    Chapter 54 Integration of Patch Features Through Self-supervised Learning and Transformer for Survival Analysis on Whole Slide Images
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    Chapter 55 Contrastive Learning Based Stain Normalization Across Multiple Tumor in Histopathology
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    Chapter 56 Semi-supervised Adversarial Learning for Stain Normalisation in Histopathology Images
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    Chapter 57 Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images
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    Chapter 58 Adversarial Learning of Cancer Tissue Representations
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    Chapter 59 A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis
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    Chapter 60 USCL: Pretraining Deep Ultrasound Image Diagnosis Model Through Video Contrastive Representation Learning
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    Chapter 61 Identifying Quantitative and Explanatory Tumor Indexes from Dynamic Contrast Enhanced Ultrasound
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    Chapter 62 Weakly-Supervised Ultrasound Video Segmentation with Minimal Annotations
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    Chapter 63 Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images
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    Chapter 64 Visual-Assisted Probe Movement Guidance for Obstetric Ultrasound Scanning Using Landmark Retrieval
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    Chapter 65 Training Deep Networks for Prostate Cancer Diagnosis Using Coarse Histopathological Labels
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    Chapter 66 Rethinking Ultrasound Augmentation: A Physics-Inspired Approach