Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

TRAMA

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. 

SOMMARIO

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.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783031164330
• Collana: Lecture Notes in Computer Science
• Dimensioni: 235 x 155 mm
• Formato: Brossura
• Illustration Notes: XL, 767 p. 218 illus., 215 illus. in color.
• Pagine Arabe: 767
• Pagine Romane: xl