Improving explainable ai with patch perturbation-based evaluation pipeline: a covid-19 x-ray image analysis case study
Recent advances in artificial intelligence (AI) have sparked interest in developing
explainable AI (XAI) methods for clinical decision support systems, especially in translational …
explainable AI (XAI) methods for clinical decision support systems, especially in translational …
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation
S Yang, P Li, X Xiong, F Shen, J Zhao - arXiv preprint arXiv:2405.11467, 2024 - arxiv.org
Data augmentation (DA) is widely employed to improve the generalization performance of
deep models. However, most existing DA methods use augmentation operations with …
deep models. However, most existing DA methods use augmentation operations with …
Discriminative feature learning with imprecise, uncertain, and ambiguous data
CH McCurley - 2022 - search.proquest.com
Target detection is a paramount task in remote sensing which aims to detect points of
interest from a set of data. A crucial aspect attributed to the success of target detection …
interest from a set of data. A crucial aspect attributed to the success of target detection …
Deep hybrid convolutional wavelet networks: application to predicting response to chemoradiation in rectal cancers via MRI
With increasing promise of radiomics and deep learning approaches in capturing subtle
patterns associated with disease response on routine MRI, there is an opportunity to more …
patterns associated with disease response on routine MRI, there is an opportunity to more …
Bag-level classification network for infrared target detection
CH McCurley, D Rodriguez… - Automatic Target …, 2022 - spiedigitallibrary.org
Aided target detection in infrared data has proven an important area of investigation for both
military and civilian applications. While target detection at the object or pixel-level has been …
military and civilian applications. While target detection at the object or pixel-level has been …