Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Hu, Shishuai"'
Addressing mixed closed-set and open-set label noise in medical image classification remains a largely unexplored challenge. Unlike natural image classification, which often separates and processes closed-set and open-set noisy samples from clean one
Externí odkaz:
http://arxiv.org/abs/2406.12293
Deep learning-based medical image segmentation models often face performance degradation when deployed across various medical centers, largely due to the discrepancies in data distribution. Test Time Adaptation (TTA) methods, which adapt pre-trained
Externí odkaz:
http://arxiv.org/abs/2405.08270
Deep learning-based medical image segmentation models suffer from performance degradation when deployed to a new healthcare center. To address this issue, unsupervised domain adaptation and multi-source domain generalization methods have been propose
Externí odkaz:
http://arxiv.org/abs/2306.05254
Manual medical image segmentation is subjective and suffers from annotator-related bias, which can be mimicked or amplified by deep learning methods. Recently, researchers have suggested that such bias is the combination of the annotator preference a
Externí odkaz:
http://arxiv.org/abs/2306.01340
Noise transition matrix (NTM) estimation is a promising approach for learning with label noise. It can infer clean posterior probabilities, known as Label Distribution (LD), based on noisy ones and reduce the impact of noisy labels. However, this est
Externí odkaz:
http://arxiv.org/abs/2212.08380
The domain discrepancy existed between medical images acquired in different situations renders a major hurdle in deploying pre-trained medical image segmentation models for clinical use. Since it is less possible to distribute training data with the
Externí odkaz:
http://arxiv.org/abs/2211.11514
Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate
Externí odkaz:
http://arxiv.org/abs/2208.13338
Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmenta
Externí odkaz:
http://arxiv.org/abs/2208.13337
Automated abdominal multi-organ segmentation is a crucial yet challenging task in the computer-aided diagnosis of abdominal organ-related diseases. Although numerous deep learning models have achieved remarkable success in many medical image segmenta
Externí odkaz:
http://arxiv.org/abs/2208.13774
Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases. Deep learning models may surpass human performance on a variety of tasks, but they may also mimic or amplify these biases. Although we can hav
Externí odkaz:
http://arxiv.org/abs/2111.13410