使无罪
发表于 2025-3-21 16:05:17
书目名称Data Augmentation, Labelling, and Imperfections影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0262734<br><br> <br><br>书目名称Data Augmentation, Labelling, and Imperfections影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0262734<br><br> <br><br>书目名称Data Augmentation, Labelling, and Imperfections网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0262734<br><br> <br><br>书目名称Data Augmentation, Labelling, and Imperfections网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0262734<br><br> <br><br>书目名称Data Augmentation, Labelling, and Imperfections被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0262734<br><br> <br><br>书目名称Data Augmentation, Labelling, and Imperfections被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0262734<br><br> <br><br>书目名称Data Augmentation, Labelling, and Imperfections年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0262734<br><br> <br><br>书目名称Data Augmentation, Labelling, and Imperfections年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0262734<br><br> <br><br>书目名称Data Augmentation, Labelling, and Imperfections读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0262734<br><br> <br><br>书目名称Data Augmentation, Labelling, and Imperfections读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0262734<br><br> <br><br>
笨重
发表于 2025-3-22 00:08:02
,Zero-Shot Learning of Individualized Task Contrast Prediction from Resting-State Functional Connect large language models using special inputs to obtain answers for novel natural language processing tasks, inputting group-average contrasts guides the OPIC model to generalize to novel tasks unseen in training. Experimental results show that OPIC’s predictions for novel tasks are not only better th
A简洁的
发表于 2025-3-22 01:17:21
,Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis,d by object level consistency; (3) the pseudo masks along with the synthetic images then constitute a pairwise dataset for training an ad-hoc segmentation model. On the public MoNuSeg dataset, our synthesis pipeline produces more diverse and realistic images than baseline models while maintaining hi
旅行路线
发表于 2025-3-22 04:45:48
,A Unified Approach to Learning with Label Noise and Unsupervised Confidence Approximation,datasets. UCA’s prediction accuracy increases with the required level of confidence. UCA-equipped networks are on par with the state-of-the-art in noisy label training when used in regular, full coverage mode. However, they have a risk-management facility, showing flawless risk-coverage curves with
举止粗野的人
发表于 2025-3-22 12:40:37
Transesophageal Echocardiography Generation Using Anatomical Models,ynthetic images quantitatively using the Fréchet Inception Distance (FID) Score and qualitatively through a human perception quiz involving expert cardiologists and the average researcher..In this study, we achieve a dice score improvement of up to 10% when we augment datasets with our synthetic ima
Fibroid
发表于 2025-3-22 16:44:12
,Data Augmentation Based on DiscrimDiff for Histopathology Image Classification,ing significance for pathologists in clinical diagnosis. Therefore, we visualize histomorphological features related to classification, which can be used to assist pathologist-in-training education and improve the understanding of histomorphology.
Fibroid
发表于 2025-3-22 20:55:18
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清真寺
发表于 2025-3-22 21:19:23
,Knowledge Graph Embeddings for Multi-lingual Structured Representations of Radiology Reports,ly more accurate, without reliance on large pre-training datasets. We show the use of this embedding on two tasks namely disease classification of X-ray reports and image classification. For disease classification our model is competitive with its BERT-based counterparts, while being magnitudes smal
招募
发表于 2025-3-23 01:51:07
,Masked Conditional Diffusion Models for Image Analysis with Application to Radiographic Diagnosis ombines the weighted segmentation masks of the tibias and the CML fracture sites as additional conditions for classifier guidance. The augmented images from our model improved the performances of ResNet-34 in classifying normal radiographs and those with CMLs. Further, the augmented images and their
AROMA
发表于 2025-3-23 06:17:08
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