Titlebook: Computational Mathematics Modeling in Cancer Analysis; Second International Wenjian Qin,Nazar Zaki,Chao Li Conference proceedings 2023 The

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https://doi.org/10.1007/978-3-031-45087-7Computer Science; Cancer imaging analysis; Computer-aided tumor detection; Multi-modality; Mathematics m

Ankylo-

978-3-031-45086-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

Tdd526

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

牙齒

FLAX

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

宴會(huì)

,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.

增強(qiáng)

ellagic-acid

,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

不可思議

STRIA

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