Jun 3, 2024 · In this work, we present an efficient, adaptable, and gradient-free method to enhance the robustness of learning-based segmentation models across training.

Our method combines the information granularity of sensitivity analysis-based methods and the scalability of data augmentation methods, which run on-the-fly ...

Jun 3, 2024 · In this work, we present an efficient, adaptable, and gradient-free method to enhance the robustness of learning-based segmentation models ...

Jun 17, 2024 · This paper introduces a sensitivity-informed data augmentation technique to improve the robustness and generalization of deep learning models ...

Co-authors ; Sensitivity-Informed Augmentation for Robust Segmentation. L Zheng, W Wei, T Wu, J Clements, S Revankar, A Harrison, Y Shen, ... arXiv preprint ...

DNA test data to increase the sensitivity of the experiment. In the first scenario, we trained baseline models using in-focus DNA image data and tested ...

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the ...

Jun 3, 2024 · 在本文中，我们提出了一个有效、可扩展且无需训练的方法，以增强基于学习的分割模型的鲁棒性。首先，我们引入了一种新的自适应灵敏度分析（ASA）来对预训练分割 ...

Nov 18, 2022 · In this paper, we report two findings that substantially improve image segmentation of tissues using a range of machine learning architectures.

Sensitivity-Informed Augmentation for Robust Segmentation · 图表 · 解决问题. 论文旨在提高学习型分割模型的鲁棒性，以应对外部和内部噪声的影响，从而在视觉计算和计算机 ...