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

性上癮

,URL: Combating Label Noise for?Lung Nodule Malignancy Grading,y degrades the performance and generalizability of models. Although researchers adopt the label-noise-robust methods to handle label noise for lung nodule malignancy grading, they do not consider the inherent ordinal relation among classes of this task. To model the ordinal relation among classes to

建筑師

Nerve-Block

種類

engrossed

Transesophageal Echocardiography Generation Using Anatomical Models,ounts of high-quality data to produce accurate results, which is difficult to satisfy. Data augmentation is commonly used to tackle this issue. In this work, we develop a pipeline to generate synthetic TEE images and corresponding semantic labels. The proposed data generation pipeline expands on an

逢迎春日

親密

calumniate

NOCT

向宇宙

,Modular, Label-Efficient Dataset Generation for?Instrument Detection for?Robotic Scrub Nurses, demand large amounts of annotated data, whose creation is expensive and time-consuming. In this work, we propose a strategy based on the copy-paste technique for the generation of reliable synthetic image training data with a minimal amount of annotation effort. Our approach enables the efficient i