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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Nutrient Management Under Changing Climateynthetic 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

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Mohamed A. M. Osman,Mohamed A. Shebling 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.

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https://doi.org/10.1007/978-3-030-41629-4respect to their detection and localisation accuracy, by assigning the corresponding report sentence where a clinically relevant anomaly is correctly detected, and rating localisation according to a 3-point scale (good, partial, poor). We find that neither method exhibits sufficiently high recall fo

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Tsugihiro Watanabe,Selim Kapur,Erhan Ak?aly 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

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Upendra Kumar,Subhra Parija,Megha Kavirajmbines 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

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