无能性
发表于 2025-3-25 03:56:06
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks has an ability to reduce falsely predicted labels and produce smooth boundaries of lung fields. We evaluate the proposed model on a common benchmark dataset, JSRT, and achieve the state-of-the-art segmentation performances with much fewer model parameters.
degradation
发表于 2025-3-25 08:38:59
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一瞥
发表于 2025-3-25 14:33:04
Conference proceedings 2017d at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support..
谄媚于人
发表于 2025-3-25 18:49:14
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Axillary
发表于 2025-3-25 19:57:51
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Collected
发表于 2025-3-26 02:45:06
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Filibuster
发表于 2025-3-26 05:17:56
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补助
发表于 2025-3-26 08:45:29
JingMin Huang,Gianluca Stringhini,Peng Yong has an ability to reduce falsely predicted labels and produce smooth boundaries of lung fields. We evaluate the proposed model on a common benchmark dataset, JSRT, and achieve the state-of-the-art segmentation performances with much fewer model parameters.
B-cell
发表于 2025-3-26 13:00:17
Alessandro Erba,Nils Ole Tippenhaueraining in a semi-supervised setting. Using two types of medical imaging data (liver CT and left ventricle MRI data), we show that the integrated method achieves good performance even when little training data is available, outperforming the FCN or the level set model alone.
兵团
发表于 2025-3-26 20:30:06
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