Titlebook: Medical Image Learning with Limited and Noisy Data; First International Ghada Zamzmi,Sameer Antani,Zhiyun Xue Conference proceedings 2022

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书目名称Medical Image Learning with Limited and Noisy Data影响因子(影响力)




书目名称Medical Image Learning with Limited and Noisy Data影响因子(影响力)学科排名




书目名称Medical Image Learning with Limited and Noisy Data网络公开度




书目名称Medical Image Learning with Limited and Noisy Data网络公开度学科排名




书目名称Medical Image Learning with Limited and Noisy Data被引频次




书目名称Medical Image Learning with Limited and Noisy Data被引频次学科排名




书目名称Medical Image Learning with Limited and Noisy Data年度引用




书目名称Medical Image Learning with Limited and Noisy Data年度引用学科排名




书目名称Medical Image Learning with Limited and Noisy Data读者反馈




书目名称Medical Image Learning with Limited and Noisy Data读者反馈学科排名

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Medical Image Learning with Limited and Noisy Data978-3-031-16760-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

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PHON

Conference proceedings 2022with MICCAI 2022. The conference was held in Singapore. For this workshop, 22 papers from 54 submissions were accepted for publication. They selected papers focus on the challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data..

Medicare

https://doi.org/10.1007/978-3-031-16760-7Computer Science; Informatics; Conference Proceedings; Research; Applications

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寄生虫

Heatmap Regression for Lesion Detection Using Pointwise Annotationsection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset.

Ferritin

朋党派系

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0302-9743 selected papers focus on the challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data..978-3-031-16759-1978-3-031-16760-7Series ISSN 0302-9743 Series E-ISSN 1611-3349