Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 : 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part II / edited by Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li.

Format:

Book

Contributor:

Wang, Linwei, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 13432

Language:

English

Subjects (All):

Image processing.

Image Processing.

Local Subjects:

Image Processing.

Physical Description:

1 online resource (802 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

Summary:

The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies. .

Contents:

Computational (Integrative) Pathology

Semi-supervised histological image segmentation via hierarchical consistency enforcement

Federated Stain Normalization for Computational Pathology

DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification

ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification

S3R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification

Distilling Knowledge from Topological Representations for Pathological Complete Response Prediction

SETMIL: Spatial Encoding Transformer-based Multiple Instance Learning for Pathological Image Analysis

Clinical-realistic Annotation for Histopathology Images with Probabilistic Semi-supervision: A Worst-case Study

End-to-end Learning for Image-based Detection of Molecular Alterations in Digital Pathology

S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning

Sample hardness based gradient loss for long-tailed cervical cell detection

Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology

Predicting molecular traits from tissue morphology through self-interactive multi-instance learning

InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation

Improved Domain Generalization for Cell Detection in Histopathology Images via Test-Time Stain Augmentation

Transformer based multiple instance learning for weakly supervised histopathology image segmentation

GradMix for nuclei segmentation and classification in imbalanced pathology image datasets

Spatial-hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification

Gigapixel Whole-Slide Images Classification using Locally Supervised Learning

Whole Slide Cervical Cancer Screening Using Graph Attention Network and Supervised Contrastive Learning

RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization

Identify Consistent Imaging Genomic Biomarkers for Characterizing the Survival-associated Interactions between Tumor-infiltrating Lymphocytes and Tumors

Semi-Supervised PR Virtual Staining for Breast Histopathological Images

Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology

Weakly Supervised Segmentation by Tensor Graph Learning for Whole Slide Images

Test Time Transform Prediction for Open Set Histopathological Image Recognition

Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis

Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification

Joint Region-Attention and Multi-Scale Transformer for Microsatellite Instability Detection from Whole Slide Images in Gastrointestinal Cancer

Self-Supervised Pre-Training for NucleiSegmentation

LifeLonger: A Benchmark for Continual Disease Classification

Unsupervised Nuclei Segmentation using Spatial Organization Priors

Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer’s Disease using weakly annotated whole slide histopathological images

MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation

Region-guided CycleGANs for Stain Transfer in Whole Slide Images

Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology Image Classification

Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction

Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling

Prostate Cancer Histology Synthesis using StyleGAN Latent Space Annotation

Fast FF-to-FFPE Whole Slide Image Translation via Laplacian Pyramid and Contrastive Learning

Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification

Computational Anatomy and Physiology

Physiological Model based Deep Learning Framework for Cardiac TMP Recovery

DentalPointNet: Landmark Localization on High-Resolution 3D Digital Dental Models

Landmark-free Statistical Shape Modeling via Neural Flow Deformations

Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection

From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach

Opthalmology

Structure-consistent Restoration Network for Cataract Fundus Image Enhancement

Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization

Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network

A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT

DA-Net: Dual Branch Transformer and Adaptive Strip Upsampling for Retinal Vessels Segmentation

Visual explanations for the detection of diabetic retinopathy from retinal fundus images

Multidimensional Hypergraph on Delineated Retinal Features for Pathological Myopia Task

Unsupervised Lesion-Aware Transfer Learning for Diabetic Retinopathy Grading in Ultra-Wide-Field Fundus Photography

Local-Region and Cross-Dataset Contrastive Learning for Retinal Vessel Segmentation

Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation

Camera Adaptation for Fundus-Image-Based CVD Risk Estimation

Opinions Vary? Diagnosis First!

Learning self-calibrated optic disc and cup segmentation from multi-rater annotations

TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes

DRGen: Domain Generalization in Diabetic Retinopathy Classification

Frequency-Aware Inverse-Consistent Deep Learning for OCT-Angiogram Super-Resolution

A Multi-task Network with Weight Decay Skip Connection Training for Anomaly Detection in Retinal Fundus Images

Multiscale Unsupervised Retinal Edema Area Segmentation in OCT Images

SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer

Screening of Dementia on OCTA Images via Multi-projection Consistency and Complementarity

Noise transfer for unsupervised domain adaptation of retinal OCT images

Long-tailed Multi-label Retinal Diseases Recognition via Relational Learning and Knowledge Distillation

Fetal Imaging

Weakly Supervised Online Action Detection for Infant General Movements

Super-Focus: Domain Adaptation for Embryo Imaging via Self-Supervised Focal Plane Regression

SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency

Automated Classification of General Movements in Infants Using Two-stream Spatiotemporal Fusion Network.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Wang, Linwei Medical Image Computing and Computer Assisted Intervention - MICCAI 2022

ISBN:

9783031164347

3031164342

OCLC:

1345280164