FedEvPrompt reports 77.26% average balanced accuracy on a 6-client federated ISIC2019 binary skin-lesion task by sharing uncertainty-selected attention maps, outperforming FedAvg and FedProx without sharing model parameters.
3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images
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abstract
Accurate representation of tooth position is extremely important in treatment. 3D dental image segmentation is a widely used method, however labelled 3D dental datasets are a scarce resource, leading to the problem of small samples that this task faces in many cases. To this end, we address this problem with a pretrained SAM and propose a novel 3D-U-SAM network for 3D dental image segmentation. Specifically, in order to solve the problem of using 2D pre-trained weights on 3D datasets, we adopted a convolution approximation method; in order to retain more details, we designed skip connections to fuse features at all levels with reference to U-Net. The effectiveness of the proposed method is demonstrated in ablation experiments, comparison experiments, and sample size experiments.
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Evidential Federated Learning for Skin Lesion Image Classification
FedEvPrompt reports 77.26% average balanced accuracy on a 6-client federated ISIC2019 binary skin-lesion task by sharing uncertainty-selected attention maps, outperforming FedAvg and FedProx without sharing model parameters.