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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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發(fā)表于 2025-3-21 19:47:37 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Applications of Medical Artificial Intelligence
期刊簡稱Second International
影響因子2023Shandong Wu,Behrouz Shabestari,Lei Xing
視頻videohttp://file.papertrans.cn/160/159499/159499.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Applications of Medical Artificial Intelligence; Second International Shandong Wu,Behrouz Shabestari,Lei Xing Conference proceedings 2024 T
影響因子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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,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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