Applications of Medical Artificial Intelligence

Name: Applications of Medical Artificial Intelligence | First International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings
Brand: Springer
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First International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

Shandong Wu Behrouz Shabestari Lei Xing(Herausgeber*in)

Springer (Verlag)

Erschienen am 30. September 2022

VIII, 162 Seiten

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978-3-031-17721-7 (ISBN)

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Inhalt

Intro
Preface
Organization
Contents
Increasing the Accessibility of Peripheral Artery Disease Screening with Deep Learning
1 Problem
2 Related Work
3 Data Collection Study
4 System Development
5 Validation Study
6 Conclusion
References
Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA
1 Introduction
2 Automated Assessment of CAD in CCTA
2.1 Straightened Representation of the Coronary Vessels
2.2 Representing Ground-Truth Segmentation as a 3D Mesh
2.3 Segmentation of Vessels Using U-Nets in Upsampled CTTA
2.4 Blood Flow Simulation
3 Experimental Validation
4 Conclusions and Future Work
References
Machine Learning for Dynamically Predicting the Onset of Renal Replacement Therapy in Chronic Kidney Disease Patients Using Claims Data
1 Introduction
2 Methods
2.1 Dataset Description
2.2 Task Definition
2.3 Data Representation and Processing
2.4 Model Description
2.5 Model Evaluation
3 Experiments and Results
3.1 Study Population and Dataset
3.2 Model Performance
4 Conclusions
References
Uncertainty-Aware Geographic Atrophy Progression Prediction from Fundus Autofluorescence
1 Introduction
2 Method
2.1 Data
2.2 Model Development
2.3 Uncertainty Estimation Using Deep Ensemble
3 Results
4 Conclusions
References
Automated Assessment of Renal Calculi in Serial Computed Tomography Scans
1 Introduction
1.1 Our Contributions
2 Materials and Methods
2.1 Data
2.2 Calculi Detection and Segmentation
2.3 Registration and Stone Matching
2.4 Manual Review and Tracking
2.5 Evaluation of Performance
2.6 Statistical Analysis
3 Results
3.1 Cohort Characteristics
3.2 Performance of the Stone Detection and Segmentation
3.3 Performance of Stone Tracking
4 Discussion
References
Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using Deep Learning
1 Introduction
2 Methods and Materials
2.1 Data
2.2 Prediction Models
2.3 Model Evaluation
2.4 Statistical Analysis
3 Results
4 Discussion
4.1 ORN Prediction
4.2 Study Limitations and Future Work
5 Conclusion
References
Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development
1 Introduction
2 Materials and Methods
2.1 Mammography Dataset
2.2 Bias Analysis
2.3 Bias Correction Techniques
2.4 Experimental Setup
3 Results and Discussion
4 Conclusions
References
ECG-ATK-GAN: Robustness Against Adversarial Attacks on ECGs Using Conditional Generative Adversarial Networks
1 Introduction
2 Methodology
2.1 Generator and Discriminator
2.2 Objective Function and Individual Losses
2.3 Adversarial Attacks
3 Experiments
3.1 Data Set Preparation
3.2 Hyper-parameters
3.3 Quantitative Evaluation
3.4 Qualitative Evaluation
4 Conclusions and Future Work
References
CADIA: A Success Story in Breast Cancer Diagnosis with Digital Pathology and AI Image Analysis
1 Introduction
2 Methods
2.1 Starting Point Analysis and Functional Requirement Collection
2.2 Sample Selection and Collection
2.3 Digital Image Annotation
2.4 Model Development
2.5 Model Deployment and Integration
3 Results
4 Conclusions and Future Perspectives
References
Was that so Hard? Estimating Human Classification Difficulty
1 Introduction
2 Estimating Image Difficulty
3 Datasets
4 Experiments
5 Results
6 Discussion and Conclusion
References
A Deep Learning-Based Interactive Medical Image Segmentation Framework
1 Introduction
2 Related Work
3 Applicative Scope
4 Methodology
4.1 System
4.2 Training with Dynamic Data Generation
5 Experimental Results
5.1 Setup
5.2 Automated Evaluation
5.3 User Evaluation
6 Conclusion
References
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin and Eosin-Stained Histological Images
1 Introduction
2 Method
2.1 Datasets
2.2 Segmentation and Regression Models
2.3 Pruning
2.4 Merging and Post-processing
2.5 Evaluation Metrics
3 Results and Discussion
4 Conclusion
References
Spatial Feature Conservation Networks (SFCNs) for Dilated Convolutions to Improve Breast Cancer Segmentation from DCE-MRI
1 Introduction
2 Methods
2.1 Compensation Module
2.2 Network Architecture
2.3 Performance Evaluation
2.4 Image Dataset and Data Preparation
3 Results
4 Discussion and Conclusion
References
The Impact of Using Voxel-Level Segmentation Metrics on Evaluating Multifocal Prostate Cancer Localisation
1 Introduction
2 Materials and Methods
2.1 Prostate Lesion Segmentation for Procedure Planning
2.2 Voxel-Level Segmentation Metrics
2.3 Lesion-Level Object Detection Metrics
2.4 Lesion Detection Metrics for Multifocal Segmentation Output
2.5 Correlation, Pairwise Agreement and Impact on Evaluation
3 Results
3.1 Comparison Between DSC and HD
3.2 Comparison Between Voxel- and Lesion-Level Metrics
4 Conclusion
References
OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs
1 Introduction
2 Methods
2.1 Feature Extractor
2.2 Point Detection Head
3 Experiments
3.1 Datasets
3.2 Evaluation Metrics
3.3 Implementation Details
3.4 Comparison to Other Methods
3.5 A Closer Look at ET-tube vs. T-tube Detection Performance
4 Conclusion
References
Wavelet Guided 3D Deep Model to Improve Dental Microfracture Detection
1 Introduction
2 Materials
3 Methods
4 Results and Discussion
References
Author Index

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