Liyanglzjt
commented
6 months ago 尊敬的3D-TransUNet模型的作者，我一直在关注您的Transunet系列的工作，并感谢您在医学图像分割领域的无私贡献。当我根据论文中的内容和代码使用仅编码器模式训练 Synapse 数据集时，结果与论文中的结果有很大不同，我认为这可能是由于数据或数据处理不同造成的。有没有办法获取你使用的 Synapse 数据集或你所做的预处理？我目前正在使用的 Synapse 数据集已被过滤为“TransUNet”：Transformers Make Strong Encoders for Medical Image Segmentation“，并根据 nnunetv1 对 30 例进行了预处理。然而，除了论文中的器官外，数据集的标签还包括 eso、IVC、venis、rad 和 lad。目前尚不清楚这是否会影响结果。感谢您的时间和帮助。
Dear blogger, I am a deep learning enthusiast, I didn't debug the code released by the author, I saw that you debugged it, can you share a copy of the complete code with me? My email is 3034964892@qq.com, thank you so much!
MohammedAdelFahmi
Dear Authors of the 3D-TransUNet Model, I have been following the work of your Transunet series and thank you for your selfless contributions in the field of medical image segmentation. When I trained the Synapse dataset using the encoder only pattern according to the content and code in the paper, the results were very different from those in the paper, which I think may be caused by different data or data processing. Is there a way to get the Synapse datasets you use or the preprocessing you do? The Synapse dataset I am currently working with has been filtered to 'TransUNet' : Transformers Make Strong Encoders for Medical Image Segmentation ", and 30 cases were pretreated according to nnunetv1. However, the labels of the data set include eso,IVC,venis,rad and lad in addition to the organs in the paper. It is unknown whether this will affect the results. Thank you for your time and assistance.