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Medical Image Learning with Limited and Noisy Data Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

Aus der Reihe Lecture Notes in Computer Science
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114,99 € UVP 131,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

31.10.2023

Abbildungen

XI, 270 p. 77 illus., 72 illus. in color.

Herausgeber

Zhiyun Xue + weitere

Verlag

Springer

Seitenzahl

270

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

435 g

Auflage

1st ed. 2023

Sprache

Englisch

ISBN

978-3-031-47196-4

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

31.10.2023

Abbildungen

XI, 270 p. 77 illus., 72 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

270

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

435 g

Auflage

1st ed. 2023

Sprache

Englisch

ISBN

978-3-031-47196-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Medical Image Learning with Limited and Noisy Data
  • Produktbild: Medical Image Learning with Limited and Noisy Data
  • Efficient Annotation and Training Strategies.-  Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-quality Annotations.- ScribSD: Scribble-supervised Fetal MRI Segmentation based on Simultaneous Feature and Prediction Self-Distillation.- Label-efficient Contrastive Learning-based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images.- Affordable Graph Neural Network Framework using Topological Graph Contraction.-  Approaches for Noisy, Missing, and Low Quality Data.-  Dual-domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-angle Reconstruction of Low-dose Cardiac SPECT.- A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging.- COVID-19 Lesion Segmentation Framework for the Contrast-enhanced CT in the Absence of Contrast-enhanced CT Annotation.- Feasibility of Universal Anomaly Detection without Knowingthe Abnormality in Medical Image.-  Unsupervised, Self-supervised, and Contrastive Learning.-  Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection.- FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation.- Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy.- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.- SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction.- Robust Unsupervised Image to Template Registration Without Image Similarity Los.- A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue.-  Weakly-supervised, Semi-supervised, and Multitask Learning.-  Combining Weakly Supervised Segmentation with Multitask Learning forImproved 3D MRI Brain Tumour Classification.-  Exigent Examiner and Mean Teacher: An Advanced 3D CNN-based Semi-Supervised Brain Tumor Segmentation Framework.- Extremely Weakly-supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation.- Multi-Task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Image.-  Active Learning.-  Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach.-    Test-time Augmentation-based Active Learning and Self-training for Label-efficient Segmentation.- Active Transfer Learning for 3D Hippocampus Segmentation.-  Transfer Learning.-  Using Training Samples as Transitive Information Bridges in Predicted 4D MRI.- To Pretrain or not to Pretrain? A Case Study of Domain-Specific Pretraining for Semantic Segmentation in Histopathology.- Large-scale Pretraining on Pathological Images for Fine-tuning of Small Pathological Benchmarks.