Titlebook: Data Augmentation, Labelling, and Imperfections; Third MICCAI Worksho Yuan Xue,Chen Chen,Yihao Liu Conference proceedings 2024 The Editor(s

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書目名稱Data Augmentation, Labelling, and Imperfections影響因子(影響力)




書目名稱Data Augmentation, Labelling, and Imperfections影響因子(影響力)學(xué)科排名




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書目名稱Data Augmentation, Labelling, and Imperfections被引頻次




書目名稱Data Augmentation, Labelling, and Imperfections被引頻次學(xué)科排名




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書目名稱Data Augmentation, Labelling, and Imperfections讀者反饋




書目名稱Data Augmentation, Labelling, and Imperfections讀者反饋學(xué)科排名

笨重

,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簡潔的

,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

旅行路線

,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

舉止粗野的人

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

,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

清真寺

,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

,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