mortality
发表于 2025-3-21 18:04:28
书目名称Medical Image Understanding and Analysis影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0629286<br><br> <br><br>书目名称Medical Image Understanding and Analysis影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0629286<br><br> <br><br>书目名称Medical Image Understanding and Analysis网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0629286<br><br> <br><br>书目名称Medical Image Understanding and Analysis网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0629286<br><br> <br><br>书目名称Medical Image Understanding and Analysis被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0629286<br><br> <br><br>书目名称Medical Image Understanding and Analysis被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0629286<br><br> <br><br>书目名称Medical Image Understanding and Analysis年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0629286<br><br> <br><br>书目名称Medical Image Understanding and Analysis年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0629286<br><br> <br><br>书目名称Medical Image Understanding and Analysis读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0629286<br><br> <br><br>书目名称Medical Image Understanding and Analysis读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0629286<br><br> <br><br>
BRIEF
发表于 2025-3-21 20:35:02
http://reply.papertrans.cn/63/6293/629286/629286_2.png
摊位
发表于 2025-3-22 01:35:35
http://reply.papertrans.cn/63/6293/629286/629286_3.png
neolith
发表于 2025-3-22 08:37:42
http://reply.papertrans.cn/63/6293/629286/629286_4.png
CRAMP
发表于 2025-3-22 09:08:09
http://reply.papertrans.cn/63/6293/629286/629286_5.png
方便
发表于 2025-3-22 15:28:59
Segmenting Hepatocellular Carcinoma in Multi-phase CTapply input-level fusion for stacks of multi-phase data as channel input. Finally, we make use of a public single-phase CT liver tumour dataset for the pre-training of network parameters to improve the generalisation capabilities of our networks.
gospel
发表于 2025-3-22 17:35:34
On New Convolutional Neural Network Based Algorithms for Selective Segmentation of Images presence of low contrast, when given suitable user input. In addition, we implement a deep learning algorithm based on this model, allowing for a supervised, semi-supervised or unsupervised approach, depending on data availability.
glucagon
发表于 2025-3-23 00:37:26
http://reply.papertrans.cn/63/6293/629286/629286_8.png
Hypomania
发表于 2025-3-23 03:49:09
http://reply.papertrans.cn/63/6293/629286/629286_9.png
Affable
发表于 2025-3-23 07:01:25
Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentationxpect that the proposed training scheme would be applicable to any feedforward network and task. We show that the network can be used to harmonise two datasets and also show that the network is applicable in the common scenario of limited available training data, meaning that the network should be applicable for real-world segmentation problems.