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Applications of medical artificial intelligence : first International Workshop, AMAI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. AMAI (Workshop) (1st : 2022 : Singapore ; Online)
- Author(s):
- Wu, Shandong, editor
- Shabestari, Behrouz, editor
- Xing, Lei, editor
- AMAI (Workshop) (1st 2022 Singapore Online)
- International Conference on Medical Image Computing and Computer-Assisted Intervention (25th 2022 Singapore Online)
- NLM Title Abbreviation:
- Appl Med Artif Intell (2022)
- Title(s):
- Applications of medical artificial intelligence : first International Workshop, AMAI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / Shandong Wu, Behrouz Shabestari, Lei Xing (eds.).
- Other Title(s):
- AMAI 2022
- Series:
- Lecture notes in computer science, ; 13540 ISSN 1611-3349
- Country of Publication:
- Switzerland
- Publisher:
- Cham, Switzerland : Springer, 2022.
- Description:
- 1 online resource (viii, 162 pages) : illustrations (some color).
-
Language:
- English
- ISBN:
- 9783031177217
3031177215
9783031177200
3031177207
- Electronic Links:
- Access not provided by NLM
- Summary:
- This book constitutes the refereed proceedings of the first International Workshop on Applications of Medical Artificial Intelligence, AMAI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022. The book includes 17 papers which were carefully reviewed and selected from 26 full-length submissions. Practical applications of medical AI bring in new challenges and opportunities. The AMAI workshop aims to engage medical AI practitioners and bring more application flavor in clinical, evaluation, human-AI collaboration, new technical strategy, trustfulness, etc., to augment the research and development on the application aspects of medical AI, on top of pure technical research.
- In:
- PubMed: Selected citations only
- Current Indexing Status:
- Not currently indexed for MEDLINE. Citations are for articles where the manuscript was deposited in PubMed Central (PMC) in compliance with public access policies. For further information, see Author Manuscripts in PMC.
- Notes:
- Includes author index.
Also issued in print.
- Contents:
- 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
- Other ID:
- (OCoLC)1346621623
- Collection Status:
- Not in the NLM Collection
- NLM ID:
- 9918574484506676 [Electronic Resource]