Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II

Häftad, Engelska, 2021

AvMarleen de Bruijne,Philippe C. Cattin,Stéphane Cotin,Nicolas Padoy,Stefanie Speidel,Yefeng Zheng,Caroline Essert,Stephane Cotin

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and machine learning - weakly supervised learningPart III: machine learning - advances in machine learning theory; and clinical applications - vascularPart VII: clinical applications - abdomen; and clinical applications - oncologyPart VIII: clinical applications - ophthalmology;

Produktinformation

Utgivningsdatum2021-09-24
Mått155 x 235 x 38 mm
Vikt1 042 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor662
FörlagSpringer Nature Switzerland AG
ISBN9783030871956

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Medicinsk bildbehandling inom Medicin
Biovetenskap inom Naturvetenskap och teknik
Tillämpad datateknik inom Data och it

Machine Learning - Self-Supervised Learning.- SSLP: Spatial Guided Self-supervised Learning on Pathological Images.- Segmentation of Left Atrial MR Images via Self-supervised Semi-supervised Meta-learning.- Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging.- Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations.- Self-supervised visual representation learning for histopathological images.- Contrastive Learning with Continuous Proxy Meta-Data For 3D MRI Classification.- Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning.- Self-Supervised Longitudinal Neighbourhood Embedding.- Self-Supervised Multi-Modal Alignment For Whole Body Medical Imaging.- SimTriplet: Simple Triplet Representation Learning with a Single GPU.- Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images.- SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation.- Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation.- SpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset.- Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images.- Topological Learning and Its Application to Multimodal Brain Network Integration.- One-Shot Medical Landmark Detection.- Implicit field learning for unsupervised anomaly detection in medical images.- Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images.- Contrastive Pre-training and Representation Distillation for Medical Visual Question Answering Based on Radiology Images.- Positional Contrastive Learning for Volumetric Medical Image Segmentation.- Longitudinal self-supervision to disentangle inter-patient variability from disease progression.- Self-Supervised Vessel Enhancement Using Flow-Based Consistencies.- Unsupervised Contrastive Learning of Radiomics and Deep Features for Label-Efficient Tumor Classification.- Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks.- Multimodal Representation Learning via Maximization of Local Mutual Information.- Inter-Regional High-level Relation Learning from Functional Connectivity via Self-Supervision.- Machine Learning - Semi-Supervised Learning.- Semi-supervised Left Atrium Segmentation with Mutual Consistency Training.- Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation.- Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency.- Few-Shot Domain Adaptation with Polymorphic Transformers.- Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection.- Reciprocal Learning for Semi-supervised Segmentation.- Disentangled Sequential Graph Autoencoder for Preclinical Alzheimer's Disease Characterizations from ADNI Study.- POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring.- 3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training.- Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation.- Implicit Neural Distance Representation for Unsupervised and Supervised Classification of Complex Anatomies.- 3D Graph-S2Net: Shape-Aware Self-Ensembling Network for Semi-Supervised Segmentation with Bilateral Graph Convolution.- Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation.- Neighbor Matching for Semi-supervised Learning.- Tripled-uncertainty Guided Mean Teacher model for Semi-supervised Medical Image Segmentation.- Learning with Noise: Mask-guided Attention Model for Weakly Supervised Nuclei Segmentation.- Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels.- Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation.- Functional Magnetic Resonance Imaging data augmentation through conditional ICA.- Scalable joint detection and segmentation of surgical instruments with weak supervision.- Machine Learning - Weakly Supervised Learning.- Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss.- Bounding Box Tightness Prior for Weakly Supervised Image Segmentation.- OXnet: Deep Omni-supervised Thoracic Disease Detection from Chest X-rays.- Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation.- Quality-Aware Memory Network for Interactive Volumetric Image Segmentation.- Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports.- Combining Attention-based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection.- CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Chamber Segmentation.- Observational Supervision for Medical Image Classification using Gaze Data.- Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation.- Efficient and Generic Interactive Segmentation Framework to Correct Mispredictions during Clinical Evaluation of Medical Images.- Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs.- Labels-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation.