论文标题
大批量和补丁尺寸培训用于医疗图像细分
Large Batch and Patch Size Training for Medical Image Segmentation
论文作者
论文摘要
多器官分割可以进行器官评估，说明多个器官之间的关系，并促进准确的诊断和治疗决策。但是，由于缺乏数据集和计算资源，只有很少的模型可以准确地执行细分。在AMOS2022挑战上，该挑战是一种大规模，临床和多样化的腹部多器官分割基准，我们使用多GPU分布式培训培训了具有大批量和贴剂大小的3D-UNET模型。与基线设置相比，对于具有较大批量和贴剂大小的模型的细分性能倾向于提高。通过使用经过不同设置训练的集合模型，进一步提高了准确性。这些结果为器官分割中的参数选择提供了参考。
Multi-organ segmentation enables organ evaluation, accounts the relationship between multiple organs, and facilitates accurate diagnosis and treatment decisions. However, only few models can perform segmentation accurately because of the lack of datasets and computational resources. On AMOS2022 challenge, which is a large-scale, clinical, and diverse abdominal multiorgan segmentation benchmark, we trained a 3D-UNet model with large batch and patch sizes using multi-GPU distributed training. Segmentation performance tended to increase for models with large batch and patch sizes compared with the baseline settings. The accuracy was further improved by using ensemble models that were trained with different settings. These results provide a reference for parameter selection in organ segmentation.
