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on1346621623 |
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OCoLC |
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20240808213014.0 |
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m o d |
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221005s2022 sz a o 101 0 eng d |
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| 019 |
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|a 1346533703
|a 1374607589
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|a 9783031177217
|q (electronic bk.)
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|a 3031177215
|q (electronic bk.)
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|z 9783031177200
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|q (7)
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|a 8303117726
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| 024 |
7 |
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|a 10.1007/978-3-031-17721-7
|2 doi
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| 035 |
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|a (OCoLC)1346621623
|z (OCoLC)1346533703
|z (OCoLC)1374607589
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| 050 |
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|2 bisacsh
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|a UYQV
|2 thema
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| 049 |
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|a HCDD
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| 111 |
2 |
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|a AMAI (Workshop)
|n (1st :
|d 2022 :
|c Singapore ; Online)
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| 245 |
1 |
0 |
|a Applications of medical artificial intelligence :
|b first International Workshop, AMAI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /
|c Shandong Wu, Behrouz Shabestari, Lei Xing (eds.).
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| 246 |
3 |
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|a AMAI 2022
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| 264 |
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1 |
|a Cham, Switzerland :
|b Springer,
|c 2022.
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| 300 |
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|a 1 online resource (viii, 162 pages) :
|b illustrations (some color).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|2 rdaft
|0 http://rdaregistry.info/termList/fileType/1002
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| 490 |
1 |
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|a Lecture notes in computer science,
|x 1611-3349 ;
|v 13540
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| 520 |
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|a 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.
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| 500 |
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|a Includes author index.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed October 5, 2022).
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| 505 |
0 |
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|a 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
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| 505 |
8 |
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|a 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
|
| 505 |
8 |
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|a 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
|
| 505 |
8 |
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|a 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
|
| 505 |
8 |
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|a 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
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| 650 |
|
0 |
|a Artificial intelligence
|x Medical applications
|v Congresses.
|
| 650 |
|
0 |
|a Diagnostic imaging
|x Data processing
|v Congresses.
|
| 650 |
|
7 |
|a Artificial intelligence
|x Medical applications
|2 fast
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| 650 |
|
7 |
|a Diagnostic imaging
|x Data processing
|2 fast
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| 655 |
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0 |
|a Electronic books.
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| 655 |
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7 |
|a proceedings (reports)
|2 aat
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| 655 |
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7 |
|a Conference papers and proceedings
|2 fast
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| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
|
|a Wu, Shandong,
|e editor.
|
| 700 |
1 |
|
|a Shabestari, Behrouz,
|e editor.
|
| 700 |
1 |
|
|a Xing, Lei,
|e editor.
|
| 711 |
2 |
|
|a International Conference on Medical Image Computing and Computer-Assisted Intervention
|n (25th :
|d 2022 :
|c Singapore ; Online)
|
| 776 |
0 |
8 |
|c Original
|z 3031177207
|z 9783031177200
|w (OCoLC)1342620330
|
| 830 |
|
0 |
|a Lecture notes in computer science ;
|v 13540.
|x 1611-3349
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-17721-7
|y Click for online access
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| 903 |
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|a SPRING-COMP2022
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| 994 |
|
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|a 92
|b HCD
|