دانلود کتاب Resource-Efficient Medical Image Analysis. First MICCAI Workshop, REMIA 2022 Singapore, September 22, 2022 Proceedings

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Preface
Organization
Contents
Multi-task Semi-supervised Learning for Vascular Network Segmentation and Renal Cell Carcinoma Classification
1 Introduction
2 Related Works
3 Dataset and Methods
3.1 Dataset Building
3.2 Multi-task Learning Pipeline
3.3 Evaluation
4 Experiments and Results
4.1 Backbone and MTL-SSL Method Choice
4.2 Segmentation Benchmarks of Vascular Network
4.3 Test on New Subtype of RCC and Other Cancers Dataset
5 Conclusion
References
Self-supervised Antigen Detection Artificial Intelligence (SANDI)
1 Introduction
2 Method
2.1 Datasets
2.2 Single-cell Patches Sampling
2.3 Patch Cropping and Pairing
2.4 Network Architecture and Training
2.5 Reference-based Cell Classification
2.6 Automatic Expansion of the Reference Set
3 Experimental Results
3.1 Classification Performance with Different Size of Randomly Selected Reference Set
3.2 Classification Performance with the Automatic Expanding Reference Set
4 Conclusion
References
RadTex: Learning Efficient Radiograph Representations from Text Reports
1 Introduction
2 Method
2.1 Network Architecture
2.2 Adapting VirTex to the Radiology Domain
3 Experimental Results
3.1 Datasets
3.2 Training Details
3.3 Training with Fewer Labeled Images in Downstream Tasks
3.4 Pretrained Representation Quality
3.5 Proxy Task: Generating Radiology Reports
4 Conclusions
References
Single Domain Generalization via Spontaneous Amplitude Spectrum Diversification
1 Introduction
2 Method
2.1 Spectrum Diversification Module
2.2 Adversarial Sample Generation
3 Experiments
3.1 Performance Evaluation
4 Conclusion
References
Triple-View Feature Learning for Medical Image Segmentation
1 Introduction
2 Methodology
2.1 Training Setup
2.2 Label Processing
2.3 Loss Function
3 Experiments and Results
3.1 Datasets and Experimental Setup
3.2 Evaluation and Results
4 Conclusion
References
Classification of 4D fMRI Images Using ML, Focusing on Computational and Memory Utilization Efficiency
1 Introduction
2 Related Work
3 Dataset
4 Region of Interest (ROI)
5 CNN Models
6 Transformer Model
7 Experiments
8 Training
8.1 Training Performance
9 Classification Accuracy
10 Conclusion
References
An Efficient Defending Mechanism Against Image Attacking on Medical Image Segmentation Models
1 Introduction
2 Our Methods
2.1 Improve Robustness Against Adversarial Example for Segmentation Models
2.2 Attacking Segmentation Model
3 Experiment and Results
3.1 Evaluation
4 Conclusions
References
Leverage Supervised and Self-supervised Pretrain Models for Pathological Survival Analysis via a Simple and Low-cost Joint Representation Tuning
1 Introduction
2 Methods
2.1 Supervised and Self-supervised Pretraining
2.2 Joint Representation Tuning
2.3 Evaluate Different Strategies of Using Pretrained Model
3 Experiments
3.1 Data Description
3.2 Experimental Setting
4 Results
4.1 Performance on Classification and Survival Prediction
4.2 Computational Resource
5 Ablation Studies
5.1 Different Strategies of Using Pretrained Models
5.2 Effect of Transformer
5.3 Effect of Data Augmentation
6 Conclusion
References
Pathological Image Contrastive Self-supervised Learning
1 Introduction
2 Revisiting Contrastive Learning
3 Histopathological Contrastive Learning
3.1 Properties of Histopathological Images
3.2 Stain Perturbation
3.3 Pipeline of Transforms
4 Experiments
4.1 Dataset
4.2 Implementation Details
4.3 Results
5 Conclusion
References
Investigation of Training Multiple Instance Learning Networks with Instance Sampling
1 Introduction
2 MIL and Attention-Based MIL Networks
2.1 MIL Problem Formulation
2.2 Attention-Based MIL
3 Sampling Strategies for Attention-Based MIL
3.1 Random Sampling
3.2 Adaptive Sampling
3.3 Top-k Sampling
4 Dataset, Network Architecures, and Tuning Hyper-parameters
4.1 Datasets
4.2 Network Architecture
4.3 Tuning Hyper-parameters
5 Results
6 Discussion
References
Masked Video Modeling with Correlation-Aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound
1 Introduction
2 Methodology
2.1 Masked Video Modeling
2.2 Correlation-Aware Contrastive Learning
3 Experiments and Results
4 Conclusions
References
A Self-attentive Meta-learning Approach for Image-Based Few-Shot Disease Detection
1 Introduction
2 Related Work
3 Proposed Approach
4 Experiments and Results
4.1 Experimental Settings
4.2 Few-Shot Diseases Detection Results
5 Conclusion
References
Facing Annotation Redundancy: OCT Layer Segmentation with only 10 Annotated Pixels per Layer
1 Introduction
2 Investigation on Less Annotated Data
2.1 Experimental Setting
2.2 Results and Observations
3 Annotation-Efficient Learning
4 Experiment
4.1 Experiment Setting
4.2 Performance Comparison
4.3 Ablation Study
5 Discussion and Conclusion
References
Author Index