释放
发表于 2025-3-25 04:04:09
https://doi.org/10.1007/978-3-031-45087-7Computer Science; Cancer imaging analysis; Computer-aided tumor detection; Multi-modality; Mathematics m
Ankylo-
发表于 2025-3-25 09:47:24
978-3-031-45086-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
Tdd526
发表于 2025-3-25 13:39:53
Virtual Contrast-Enhanced MRI Synthesis with High Model Generalizability Using Trusted Federated Leevention was visually assessed by reviewing the excluded images after training. Three single institutional models (separately trained with single institutional data), a joint model (jointly trained using multi-institutional data), and two popular federated learning frameworks (FedAvg and FedProx) we
牙齿
发表于 2025-3-25 17:39:43
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FLAX
发表于 2025-3-25 23:01:11
The Value of Ensemble Learning Model Based on Conventional Non-Contrast MRI in the Pathological Graaverage AUC was 0.74(0.69,0.76) and the accuracy was 0.73. It was followed by SVM, LR and KNN models, and the average AUC were 0.73(0.66,0.80), 0.71(0.62,0.78) and 0.66(0.61,0.72), respectively. The performance of stacking ensemble model showed effective improvement, with an average AUC of 0.77(0.67
宴会
发表于 2025-3-26 03:32:48
,Federated Multi-organ Dynamic Attention Segmentation Network with Small CT Dataset, clients and the unseen external testing dataset from the center server. The experimental results show that the proposed federated aggregation scheme improves the generalization ability of the model in a smaller training dataset and partially alleviates the problem of class imbalance.
增强
发表于 2025-3-26 04:35:48
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ellagic-acid
发表于 2025-3-26 08:47:09
,Advancing Delineation of Gross Tumor Volume Based on Magnetic Resonance Imaging by Performing Sourcansfers knowledge of tumor segmentation learned in the source domain to the unlabeled target dataset without the access to the source dataset and annotate the target domain, for the NPC. Specifically, We enhances model performance by jointly optimizing entropy minimization and pseudo-labeling based
不可思议
发表于 2025-3-26 16:26:55
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STRIA
发表于 2025-3-26 19:36:21
Fully Convolutional Transformer-Based GAN for Cross-Modality CT to PET Image Synthesis,lled C2P-GAN for cross-modality synthesis of PET images from CT images. It composed of a generator and a discriminator that compete with each other, as well as a registration network that can eliminate noise interference. The generator integrates convolutional networks that excel in capturing local