使增至最大
发表于 2025-3-23 09:43:14
Alessandra Battisti,Livia Calcagnilative volume difference (RVD) decreases by 1.09%; On the CHAOS dataset, the DSC of this method reaches 96.8%, and compared with CANet, the DSC increases by 0.2%; On the MRI dataset of a hospital, the DSC of this method reaches 97.2%.
colostrum
发表于 2025-3-23 17:36:00
Avant Gardening to Grow the Green Citye, so the target image usually contains some original attribute features. Therefore, a Prior-Guided Generative Adversarial Net (PGedGAN) based on the image attribute editing technology to guide complete attributes manipulation is proposed in this paper. The prior includes two parts: 1) Location Prio
SYN
发表于 2025-3-23 21:50:38
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ECG769
发表于 2025-3-23 22:18:49
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enchant
发表于 2025-3-24 04:22:18
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可憎
发表于 2025-3-24 07:29:29
https://doi.org/10.1057/9780230611832e PredACVP model, which achieves AUC of 0.990 on test datasets, outperforms the state-of-the-art tools for ACVPs identification. PredACVP can improve the prediction performance, and accelerate the development of peptide drugs for COVID-19. GAN used in this work not only can be applied on the data au
使人入神
发表于 2025-3-24 13:37:29
https://doi.org/10.1007/978-1-349-25545-0each mapping module recursively iterates twice. Finally, the new enhanced features are entered into the classifier. The output of the model represents the probability that a gene is a driver gene. We applied our method and four other existing methods to predict cancer drivers. The experimental resul
visual-cortex
发表于 2025-3-24 18:06:49
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Keratin
发表于 2025-3-24 22:45:18
https://doi.org/10.1007/978-1-349-25545-0pically requires a significant amount of data to train neural networks to identify specific features and types of glioma cells. On this basis, we proposed Cascade Fusion Network (CFNet) to try to improve the accuracy of identification of glioma cells.
endure
发表于 2025-3-25 02:49:37
Alzheimer’s Disease in American Fictionesophageal cancer entities as well as extracted entities in biomedical literature. The designed model framework is mainly composed of a BioBERT pre-training language model coding layer, batch standardization layer, head entity decoding layer, and tail entity decoding layer, as well as constructs a c