不让做的事
发表于 2025-3-21 16:04:30
书目名称Head and Neck Tumor Segmentation and Outcome Prediction影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0424585<br><br> <br><br>书目名称Head and Neck Tumor Segmentation and Outcome Prediction影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0424585<br><br> <br><br>书目名称Head and Neck Tumor Segmentation and Outcome Prediction网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0424585<br><br> <br><br>书目名称Head and Neck Tumor Segmentation and Outcome Prediction网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0424585<br><br> <br><br>书目名称Head and Neck Tumor Segmentation and Outcome Prediction被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0424585<br><br> <br><br>书目名称Head and Neck Tumor Segmentation and Outcome Prediction被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0424585<br><br> <br><br>书目名称Head and Neck Tumor Segmentation and Outcome Prediction年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0424585<br><br> <br><br>书目名称Head and Neck Tumor Segmentation and Outcome Prediction年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0424585<br><br> <br><br>书目名称Head and Neck Tumor Segmentation and Outcome Prediction读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0424585<br><br> <br><br>书目名称Head and Neck Tumor Segmentation and Outcome Prediction读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0424585<br><br> <br><br>
我就不公正
发表于 2025-3-21 21:39:06
http://reply.papertrans.cn/43/4246/424585/424585_2.png
宣传
发表于 2025-3-22 01:53:14
http://reply.papertrans.cn/43/4246/424585/424585_3.png
LEERY
发表于 2025-3-22 05:47:51
Hung Chu,Luis Ricardo De la O Arévalo,Wei Tang,Baoqiang Ma,Yan Li,Alessia De Biase,Stefan Both,JohanRe sources Research (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index i978-1-4757-0410-5978-1-4757-0408-2
Liability
发表于 2025-3-22 12:30:26
Louis Rebaud,Thibault Escobar,Fahad Khalid,Kibrom Girum,Irène Buvat sources Research (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index is 978-1-4757-0422-8978-1-4757-0420-4
Breach
发表于 2025-3-22 15:46:31
Mingyuan Meng,Lei Bi,Dagan Feng,Jinman KimResearch (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index is
仲裁者
发表于 2025-3-22 21:05:57
Kai Wang,Yunxiang Li,Michael Dohopolski,Tao Peng,Weiguo Lu,You Zhang,Jing Wang sources Research (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index is 978-1-4757-0422-8978-1-4757-0420-4
打火石
发表于 2025-3-22 21:48:14
http://reply.papertrans.cn/43/4246/424585/424585_8.png
Alveoli
发表于 2025-3-23 02:41:04
,Octree Boundary Transfiner: Efficient Transformers for Tumor Segmentation Refinement,network feature maps in addition to the raw modalities as input and selects regions of interest from these. These are then processed with a transformer network and decoded with a CNN. We evaluated our framework with Dice Similarity Coefficient (DSC) 0.76426 for the first task of the Head and Neck Tu
Guileless
发表于 2025-3-23 07:09:24
,Head and Neck Primary Tumor and Lymph Node Auto-segmentation for PET/CT Scans,l deep learning frameworks, including 3D U-Net, MNet, Swin Transformer, and nnU-Net (both 2D and 3D), to segment CT and PET images of primary tumors (GTVp) and cancerous lymph nodes (GTVn) automatically. Our investigations led us to three promising models for submission. Via 5-fold cross validation