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231103 ||| eng |
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|a 9783031449178
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| 100 |
1 |
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|a Xue, Zhiyun
|e [editor]
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| 245 |
0 |
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|a Medical Image Learning with Limited and Noisy Data
|h Elektronische Ressource
|b Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
|c edited by Zhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang
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| 250 |
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|a 1st ed. 2023
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| 260 |
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|a Cham
|b Springer Nature Switzerland
|c 2023, 2023
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| 300 |
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|a XI, 270 p. 77 illus., 72 illus. in color
|b online resource
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| 505 |
0 |
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|a 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 --
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| 505 |
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|a 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
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| 505 |
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|a 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 --
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| 653 |
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|a Image processing / Digital techniques
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| 653 |
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|a Computer vision
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| 653 |
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|a Computer Imaging, Vision, Pattern Recognition and Graphics
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| 700 |
1 |
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|a Antani, Sameer
|e [editor]
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| 700 |
1 |
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|a Zamzmi, Ghada
|e [editor]
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| 700 |
1 |
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|a Yang, Feng
|e [editor]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Lecture Notes in Computer Science
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| 028 |
5 |
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|a 10.1007/978-3-031-44917-8
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-031-44917-8?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006
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| 520 |
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|a This book consists of full papers presented in the 2nd workshop of ”Medical Image Learning with Noisy and Limited Data (MILLanD)” held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 24 full papers presented were carefully reviewed and selected from 38 submissions. The conference focused on challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data
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