LURE
发表于 2025-3-23 12:25:18
Li Zhang,Dana Cobzas,Alan Wilman,Linglong Kong komplett aktualisierten 6. Auflage: Schmerz, Geschlecht und Opioidwirkung, Bewertung transdermaler Applikationstechniken, Therapie opioidbedingter Nebenwirkungen, Opioidanwendung bei Säuglingen und alten Mensc978-3-662-09096-1
MAUVE
发表于 2025-3-23 14:58:26
http://reply.papertrans.cn/63/6293/629231/629231_12.png
整洁
发表于 2025-3-23 21:33:03
http://reply.papertrans.cn/63/6293/629231/629231_13.png
入伍仪式
发表于 2025-3-24 02:08:44
Mingliang Wang,Xiaoke Hao,Jiashuang Huang,Kangcheng Wang,Xijia Xu,Daoqiang Zhang
杠杆支点
发表于 2025-3-24 02:53:08
http://reply.papertrans.cn/63/6293/629231/629231_15.png
Flatter
发表于 2025-3-24 07:56:34
http://reply.papertrans.cn/63/6293/629231/629231_16.png
换话题
发表于 2025-3-24 13:04:55
Deep Multi-task Multi-channel Learning for Joint Classification and Regression of Brain Statuss in a data-driven manner, and then extract multiple image patches around these detected landmarks. A deep multi-task multi-channel convolutional neural network is then developed for joint disease classification and clinical score regression. We train our model on a large multi-center cohort (., ADN
exquisite
发表于 2025-3-24 18:17:52
Multi-level Multi-task Structured Sparse Learning for Diagnosis of Schizophrenia Disease classifiers. Finally, we adopt an ensemble strategy to combine outputs of all SVM classifiers to achieve the final decision. Our method has been evaluated on 46 subjects, and the superior classification results demonstrate the effectiveness of our proposed method as compared to other methods.
HIKE
发表于 2025-3-24 22:39:48
Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Daeature selection in an unified formulation, thus alleviating the modality heterogeneity issue and making all the samples comparable to share a common classifier in the RKHS. The resulting classifier obviously captures the nonlinear data-to-label relationship. We have tested our method using MRI and
arterioles
发表于 2025-3-25 02:35:43
GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction experiments have been evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The advantage of proposed model is verified by improved stability of selected lesion features and better classification results.