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Titlebook: Applications of Medical Artificial Intelligence; Second International Shandong Wu,Behrouz Shabestari,Lei Xing Conference proceedings 2024 T

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期刊全称Applications of Medical Artificial Intelligence
期刊简称Second International
影响因子2023Shandong Wu,Behrouz Shabestari,Lei Xing
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Applications of Medical Artificial Intelligence; Second International Shandong Wu,Behrouz Shabestari,Lei Xing Conference proceedings 2024 T
影响因子This book constitutes the refereed proceedings of the first International Workshop on Applications of Medical Artificial Intelligence, AMAI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada in October 2023. .The book includes 17 papers which were carefully reviewed and selected from 26 full-length submissions..The AMAI 2023 workshop created a forum to bring together researchers, clinicians, domain experts, AI practitioners, industry representatives, and students to investigate and discuss various challenges and opportunities related to applications of medical AI..
Pindex Conference proceedings 2024
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书目名称Applications of Medical Artificial Intelligence影响因子(影响力)




书目名称Applications of Medical Artificial Intelligence影响因子(影响力)学科排名




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书目名称Applications of Medical Artificial Intelligence网络公开度学科排名




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书目名称Applications of Medical Artificial Intelligence被引频次学科排名




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书目名称Applications of Medical Artificial Intelligence年度引用学科排名




书目名称Applications of Medical Artificial Intelligence读者反馈




书目名称Applications of Medical Artificial Intelligence读者反馈学科排名




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,Clinical Trial Histology Image Based End-to-End Biomarker Expression Levels Prediction and Visualizics the staining processes, surpassing prior works and offering a feasible substitute for traditional histopathology methods. Preliminary results are presented using a clinical trial dataset pertaining to the CEACAM5 biomarker.
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Investigating the Impact of Image Quality on Endoscopic AI Model Performance,, as well as on a manually selected dataset that includes images with lower subjective quality. Our findings highlight the importance of understanding the impact of a decrease in image quality and the need to include robustness evaluation for DNNs used in endoscopy.
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Ensembling Voxel-Based and Box-Based Model Predictions for Robust Lesion Detection,cer detection in multi-modal MRI. Performance is evaluated on publicly-available databases, and compared to two state-of-the art baseline methods. The proposed ensembling approach improves the average precision metric in all considered applications, with a 8% gain for prostate cancer.
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