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  • Produktbild: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
  • Produktbild: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
Band 13432

Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part II

Aus der Reihe Lecture Notes in Computer Science

Fr. 72.90

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

16.09.2022

Herausgeber

Linwei Wang + weitere

Verlag

Springer

Seitenzahl

767

Maße (L/B/H)

23.5/15.5/4.4 cm

Gewicht

1200 g

Auflage

1st edition 2022

Sprache

Englisch

ISBN

978-3-031-16433-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

16.09.2022

Herausgeber

Verlag

Springer

Seitenzahl

767

Maße (L/B/H)

23.5/15.5/4.4 cm

Gewicht

1200 g

Auflage

1st edition 2022

Sprache

Englisch

ISBN

978-3-031-16433-0

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
  • Produktbild: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

  • 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.