Titlebook: Medical Image Learning with Limited and Noisy Data; First International Ghada Zamzmi,Sameer Antani,Zhiyun Xue Conference proceedings 2022

敲竹杠

Abstraction in Pixel-wise Noisy Annotations Can Guide Attention to Improve Prostate Cancer Grade AssIs, accuracy (Acc), the quadratic weighted kappa score (QWK), and Spearman coefficient were increased by 0.71%, 5.77%, and 6.06%, respectively, while the mean absolute error (MAE) was decreased by 14.83%. We believe that the method has great potential for appropriate usage of noisy pixel-wise annotations.

rheumatism

BoxShrink: From Bounding Boxes to Segmentation Maskse label transformations. An average of four percent improvement in IoU is found across several models when being trained using BoxShrink in a weakly-supervised setting, compared to using only bounding box annotations as inputs on a colonoscopy image data set. We open-sourced the code for the proposed framework and published it online.

起来了

CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classificationour framework to a wide-array of medical imaging datasets on a variety of real-world class imbalance scenarios - namely, . imbalance and . imbalance. We show that . outperforms the state-of-the-art active learning methods by acquiring a diverse set of data points that belong to the rare classes.

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Frisky

children. Each chapter provides an overview of the problem, followed by a "hands on" description of relevant assessment and intervention strategies. The role of the primary care practitioner is highlighted, both as a front-line resource as well as a consumer of specialized pediatric pain treatment

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Bella Specktor Fadida,Daphna Link Sourani,Liat Ben Sira,Elka Miller,Dafna Ben Bashat,Leo Joskowicz children. Each chapter provides an overview of the problem, followed by a "hands on" description of relevant assessment and intervention strategies. The role of the primary care practitioner is highlighted, both as a front-line resource as well as a consumer of specialized pediatric pain treatment

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