Zobrazeno 1 - 10
of 551
pro vyhledávání: '"Zhou, Jiliu"'
BTMuda: A Bi-level Multi-source unsupervised domain adaptation framework for breast cancer diagnosis
Deep learning has revolutionized the early detection of breast cancer, resulting in a significant decrease in mortality rates. However, difficulties in obtaining annotations and huge variations in distribution between training sets and real scenes ha
Externí odkaz:
http://arxiv.org/abs/2408.17054
To acquire high-quality positron emission tomography (PET) images while reducing the radiation tracer dose, numerous efforts have been devoted to reconstructing standard-dose PET (SPET) images from low-dose PET (LPET). However, the success of current
Externí odkaz:
http://arxiv.org/abs/2407.20878
Facial Expression Recognition (FER) holds significant importance in human-computer interactions. Existing cross-domain FER methods often transfer knowledge solely from a single labeled source domain to an unlabeled target domain, neglecting the compr
Externí odkaz:
http://arxiv.org/abs/2407.05688
Radiation hazards associated with standard-dose positron emission tomography (SPET) images remain a concern, whereas the quality of low-dose PET (LPET) images fails to meet clinical requirements. Therefore, there is great interest in reconstructing S
Externí odkaz:
http://arxiv.org/abs/2406.13150
Universal Multi-source Domain Adaptation (UniMDA) transfers knowledge from multiple labeled source domains to an unlabeled target domain under domain shifts (different data distribution) and class shifts (unknown target classes). Existing solutions f
Externí odkaz:
http://arxiv.org/abs/2404.14696
To obtain high-quality positron emission tomography (PET) while minimizing radiation exposure, a range of methods have been designed to reconstruct standard-dose PET (SPET) from corresponding low-dose PET (LPET) images. However, most current methods
Externí odkaz:
http://arxiv.org/abs/2404.01563
Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation . However, most existing SSL methods predict pixels in a single image independently, ignoring
Externí odkaz:
http://arxiv.org/abs/2403.03512
Autor:
Cui, Jiaqi, Xu, Yuanyuan, Xiao, Jianghong, Fei, Yuchen, Zhou, Jiliu, Peng, Xingcheng, Wang, Yan
Deep learning has facilitated the automation of radiotherapy by predicting accurate dose distribution maps. However, existing methods fail to derive the desirable radiotherapy parameters that can be directly input into the treatment planning system (
Externí odkaz:
http://arxiv.org/abs/2402.18879
Radiotherapy is a primary treatment for cancers with the aim of applying sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs at risk (OARs). Convolutional neural networks (CNNs) have automated the
Externí odkaz:
http://arxiv.org/abs/2402.04566
To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) images from the corresponding low-dose PET (LPET) images. However, these
Externí odkaz:
http://arxiv.org/abs/2402.00376