Zobrazeno 1 - 10
of 2 250
pro vyhledávání: '"Li, Xiaomeng"'
Generative modeling seeks to approximate the statistical properties of real data, enabling synthesis of new data that closely resembles the original distribution. Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DD
Externí odkaz:
http://arxiv.org/abs/2407.03018
Cone-Beam Computed Tomography (CBCT) is an indispensable technique in medical imaging, yet the associated radiation exposure raises concerns in clinical practice. To mitigate these risks, sparse-view reconstruction has emerged as an essential researc
Externí odkaz:
http://arxiv.org/abs/2407.01090
Cross-domain recommendation offers a potential avenue for alleviating data sparsity and cold-start problems. Embedding and mapping, as a classic cross-domain research genre, aims to identify a common mapping function to perform representation transfo
Externí odkaz:
http://arxiv.org/abs/2406.16494
Cone beam computed tomography (CBCT) is an important imaging technology widely used in medical scenarios, such as diagnosis and preoperative planning. Using fewer projection views to reconstruct CT, also known as sparse-view reconstruction, can reduc
Externí odkaz:
http://arxiv.org/abs/2406.03902
Autor:
Schmidt, Kendall, Bearce, Benjamin, Chang, Ken, Coombs, Laura, Farahani, Keyvan, Elbatele, Marawan, Mouhebe, Kaouther, Marti, Robert, Zhang, Ruipeng, Zhang, Yao, Wang, Yanfeng, Hu, Yaojun, Ying, Haochao, Xu, Yuyang, Testagrose, Conrad, Demirer, Mutlu, Gupta, Vikash, Akünal, Ünal, Bujotzek, Markus, Maier-Hein, Klaus H., Qin, Yi, Li, Xiaomeng, Kalpathy-Cramer, Jayashree, Roth, Holger R.
Publikováno v:
Medical Image Analysis Volume 95, July 2024, 103206
The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography system
Externí odkaz:
http://arxiv.org/abs/2405.14900
Radiology report generation aims to automatically generate detailed and coherent descriptive reports alongside radiology images. Previous work mainly focused on refining fine-grained image features or leveraging external knowledge. However, the preci
Externí odkaz:
http://arxiv.org/abs/2405.00962
This study aimed to develop a deep learning model for the classification of bearing faults in wind turbine generators from acoustic signals. A convolutional LSTM model was successfully constructed and trained by using audio data from five predefined
Externí odkaz:
http://arxiv.org/abs/2403.09030
Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling, which leverages limited labeled data along with larger amounts of unlabeled data. Current state-of-the-art methods t
Externí odkaz:
http://arxiv.org/abs/2403.01818
Ignoring the differences between countries, human reproductive and dispersal behaviors can be described by some standardized models, so whether there is a universal law of population growth hidden in the abundant and unstructured data from various co
Externí odkaz:
http://arxiv.org/abs/2402.04612
Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept-based interpretations for black-box deep learning models. They typically work by predicting concepts given the input and then predicting the final c
Externí odkaz:
http://arxiv.org/abs/2401.14142