Domain adaptation and representation transfer, and distributed and collaborative learning : Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings.: Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings. ([2020])
- Record Type:
- Book
- Title:
- Domain adaptation and representation transfer, and distributed and collaborative learning : Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings.: Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings. ([2020])
- Main Title:
- Domain adaptation and representation transfer, and distributed and collaborative learning : Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings.
- Other Names:
- Albarqouni, Shadi
Bakas, Spyridon
Kamnitsas, Konstantinos
Cardoso, M. Jorge
Landman, Bennett
Li, Wenqi
Milletari, Fausto
Rieke, Nicola
Roth, Holger
Xu, Daguang - Contents:
- Intro -- Preface DART 2020 -- Preface DCL 2020 -- Organization -- Contents -- DART 2020 -- UNet++: A Data-Driven Neural Network Architecture for Medical Image Segmentation -- 1 Introduction -- 2 Datasets -- 3 Method -- 3.1 Pruning Decoding Blocks -- 3.2 UNet++C -- 3.3 Implementation and Evaluation -- 4 Results and Discussion -- 5 Conclusion -- References -- DAPR-Net: Domain Adaptive Predicting-Refinement Network for Retinal Vessel Segmentation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Preprocessing Operations -- 3.2 Network Architecture -- 4 Experiments and Results 4.1 Datasets -- 4.2 Baselines and Evaluation Scenarios -- 4.3 Training Strategy -- 4.4 Ablation Study -- 4.5 Results -- 5 Conclusion -- References -- Augmented Radiology: Patient-Wise Feature Transfer Model for Glioma Grading -- Abstract -- 1 Introduction -- 2 Materials and Methodology -- 2.1 Datasets and Preprocessing -- 2.2 Baseline Radiological Model -- 2.3 Proposed Method: Augmented Radiological Model -- 3 Experiments and Results -- 3.1 Training/Evaluation Details -- 4 Conclusion -- References Attention-Guided Deep Domain Adaptation for Brain Dementia Identification with Multi-site Neuroimaging Data -- 1 Introduction -- 2 Methodology -- 2.1 Problem Definition -- 2.2 Proposed Attention-Guided Deep Domain Adaptation (AD2A) -- 3 Experiments -- 4 Conclusion -- References -- Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps -- 1 Introduction -- 1.1 Contributions --Intro -- Preface DART 2020 -- Preface DCL 2020 -- Organization -- Contents -- DART 2020 -- UNet++: A Data-Driven Neural Network Architecture for Medical Image Segmentation -- 1 Introduction -- 2 Datasets -- 3 Method -- 3.1 Pruning Decoding Blocks -- 3.2 UNet++C -- 3.3 Implementation and Evaluation -- 4 Results and Discussion -- 5 Conclusion -- References -- DAPR-Net: Domain Adaptive Predicting-Refinement Network for Retinal Vessel Segmentation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Preprocessing Operations -- 3.2 Network Architecture -- 4 Experiments and Results 4.1 Datasets -- 4.2 Baselines and Evaluation Scenarios -- 4.3 Training Strategy -- 4.4 Ablation Study -- 4.5 Results -- 5 Conclusion -- References -- Augmented Radiology: Patient-Wise Feature Transfer Model for Glioma Grading -- Abstract -- 1 Introduction -- 2 Materials and Methodology -- 2.1 Datasets and Preprocessing -- 2.2 Baseline Radiological Model -- 2.3 Proposed Method: Augmented Radiological Model -- 3 Experiments and Results -- 3.1 Training/Evaluation Details -- 4 Conclusion -- References Attention-Guided Deep Domain Adaptation for Brain Dementia Identification with Multi-site Neuroimaging Data -- 1 Introduction -- 2 Methodology -- 2.1 Problem Definition -- 2.2 Proposed Attention-Guided Deep Domain Adaptation (AD2A) -- 3 Experiments -- 4 Conclusion -- References -- Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps -- 1 Introduction -- 1.1 Contributions -- 2 Method -- 2.1 Self Supervised Segmentation Feature Maps -- 2.2 Registration Using Segmentation Maps -- 3 Experimental Results -- 3.1 Implementation and Dataset Details 3.2 ANHIR Registration Results -- 3.3 Brain Image Registration -- 4 Conclusion -- References -- Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content Space -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 Overall Framework -- 2.3 Disentanglement Learning Module -- 2.4 Self-supervision Module -- 2.5 Segmentation Module -- 2.6 Implementation -- 3 Experiments -- 3.1 Datasets and Evaluation Metric -- 3.2 Experiment Settings -- 3.3 Results and Analysis -- 4 Conclusions -- References Semi-supervised Pathology Segmentation with Disentangled Representations -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Pathology Disentanglement -- 3.2 APD-Net Architecture -- 3.3 Individual Training Losses -- 3.4 Joint Optimization with Teacher-Forcing Training Strategy -- 4 Experiments -- 4.1 Results and Discussion -- 5 Conclusions and Future Work -- References -- Domain Generalizer: A Few-Shot Meta Learning Framework for Domain Generalization in Medical Imaging -- 1 Introduction and Background -- 2 Methodology -- 3 Experiments -- 3.1 Databases -- 3.2 Experimental Setup … (more)
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2020
- Extent:
- 1 online resource
- Subjects:
- 006.3/1
Machine learning -- Congresses
Electronic books - Languages:
- English
- ISBNs:
- 9783030605483
3030605485 - Access Rights:
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