Titlebook: Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for; 10th Workshop, CLIP Cristina Oyarzun

同步信息

可行

ViTBIS: Vision Transformer for Biomedical Image SegmentationMRI segmentation dataset and Spleen CT segmentation dataset. Without bells and whistles, our network outperforms most of the previous state of the art CNN and transformer based models using Dice score and the Hausdorff distance as the evaluation metrics.

quiet-sleep

https://doi.org/10.1007/978-981-16-4023-0on accuracy and system usefulness. In addition, the proposed method’s inference was 0.5 min on average, around 8-times faster than the 3D U-Net’s. Although subsequent processes still require more than 20 min for each case, utilizing lightweight networks is essential for practical use in the future, especially for emergency diagnosis.

Congruous

https://doi.org/10.1007/978-981-16-4023-0 a Sparse Graph Variational Auto-encoder. Our model can detect the latent communities between EEG channels in a non-parametric Bayesian way and provide uncertainty information of model prediction. Extensive experiments have been conducted to justify the effectiveness of our model and the results show that uncertainty information can help a lot.

绊住

Rural migrants in urban settingaforementioned multi-task scenarios. In this paper, we investigate heterogeneous optimization methods that show improvements for the automated segmentation of pancreas and pancreatic tumors in abdominal CT images with FL settings.

无能的人

atrophy

节约

Multi-task Federated Learning for Heterogeneous Pancreas Segmentationaforementioned multi-task scenarios. In this paper, we investigate heterogeneous optimization methods that show improvements for the automated segmentation of pancreas and pancreatic tumors in abdominal CT images with FL settings.

Free-Radical

Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Plces demonstrate vertebrae and intervertebral disc fitting accuracy of the automatic methodology. In severe AIS cases, when CNN segmentations fail due to overfitting, a semi-automatic step augments the automatic method. The generated FE meshes can provide the basis for biomechanical simulations seeki

deface

Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumescessary features. We employ a multi-scale aggregation to combine the information at different scales to improve the segmentation performance further. Our proposed multi-scale pancreatic attention-guided approach achieved a Dice score of 54.16% on dilated pancreatic duct dataset, which shows a signif