Published: Springer, 2021
Description:
1 online resource (xxxviii, 704 pages) : illustrations (chiefly color).
Contents:
“…Clinical Applications -- Ophthalmology --
Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition -- Cross-domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography -- Distinguishing Differences Matters: Focal Contrastive Network for Peripheral Anterior Synechiae Recognition -- RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network -- MIL-VT: Multiple Instance Learning Enhanced Vision Transformer for Fundus Image Classification -- Local-global Dual Perception based Deep Multiple Instance Learning for Retinal Disease Classification -- BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images -- LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos -- I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive constraining -- Few-shot Transfer Learning for Hereditary Retinal Diseases Recognition -- Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images -- Computational (Integrative) Pathology -- GQ-GCN: Group Quadratic Graph Convolutional Network for Classification of Histopathological Images -- Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network -- Prototypical models for classifying high-risk atypical breast lesions -- Hierarchical Attention Guided Framework for Multi-resolution Collaborative Whole Slide Image Segmentation -- Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms -- A computational geometry approach for modeling neuronal fiber pathways -- TransPath: Transformer-based Self-supervised Learning for Histopathological Image Classification -- From Pixel to Whole Slide: Automatic Detection of Microvascular Invasion in Hepatocellular Carcinoma on Histopathological Image via Cascaded Networks -- DT-MIL: Deformable Transformer for Multi-instance Learning on Histopathological Image -- Early Detection of Liver Fibrosis Using Graph Convolutional Networks -- Hierarchical graph pathomic network for progression free survival prediction -- Increasing Consistency of Evoked Response in Thalamic Nuclei During Repetitive Burst Stimulation of Peripheral Nerve in
Humans -- Weakly supervised pan-cancer segmentation tool -- Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations -- MetaCon: Meta Contrastive Learning for Microsatellite Instability Detection -- Generalizing Nucleus Recognition Model in Multi-source Ki67 Immunohistochemistry Stained Images via Domain-specific Pruning -- Cells are Actors:
Social Network Analysis with Classical ML for SOTA Histology Image Classification -- Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image -- Hybrid Supervision Learning for Whole Slide Image Classification -- MorphSet: Improving Renal Histopathology Case Assessment Through Learned Prognostic Vectors -- Accounting for Dependencies in Deep Learning based Multiple Instance Learning for Whole Slide Imaging -- Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks -- Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images -- Modalities -- Microscopy -- Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field -- Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency -- Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap -- 2D Histology Meets 3D Topology: Cytoarchitectonic Brain Mapping with Graph Neural Networks -- Annotation-efficient Cell Counting -- A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos -- Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification -- Learning Neuron Stitching for Connectomics -- CÂ{2.5}-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection -- Automated Malaria Cells Detection from Blood Smears under Severe Class Imbalance via Importance-aware Balanced Group Softmax -- Non-parametric vignetting correction for sparse spatial transcriptomics images -- Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy -- Deep Reinforcement Exemplar Learning for Annotation Refinement -- Modalities -- Histopathology -- Instance-aware Feature Alignment for Cross-domain Cell Nuclei Detection in Histopathology Images -- Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations -- GloFlow: Whole Slide Image Stitching from Video using Optical Flow and Global Image Alignment -- Multi-modal Multi-instance Learning using Weakly Correlated Histopathological Images and Tabular Clinical Information -- Ranking loss: A ranking-based deep neural network for colorectal cancer grading in pathology images -- Spatial Attention-based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains -- Integration of Patch Features through Self-Supervised Learning and Transformer for Survival Analysis on Whole Slide Images -- Contrastive Learning Based Stain Normalization Across Multiple Tumor Histopathology -- Semi-supervised Adversarial Learning for Stain Normalisation in Histopathology Images -- Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images -- Adversarial learning of cancer tissue representations -- A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis -- Modalities -- Ultrasound -- USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning -- Identifying Quantitative and Explanatory Tumor Indexes from Dynamic Contrast Enhanced Ultrasound -- Weakly-Supervised Ultrasound Video Segmentation with Minimal Annotations -- Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images -- Visual-Assisted Probe Movement Guidance for Obstetric Ultrasound Scanning using Landmark Retrieval -- Training Deep Networks for Prostate Cancer Diagnosis Using Coarse Histopathological Labels -- Rethinking Ultrasound Augmentation: A Physics-Inspired Approach.…”
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