By another round of training based on previous weights, our model also achieved a better performance on ACDC (91.61% DSC). We have changed the weight for ACDC to this newest version and you can check it out for yourself. However, previous versions of weights are still available on Google Drive, and you can access them via previous commits.
We have further trained our MT-UNet and it turns out to have a better result on Synapse with 79.20% DSC. We have changed the public weights of Synapse to this version and will also update the results in our paper.
It should be mentioned that we are currently conducting some statistical evaluations on our model and these results will be also made public on this site.
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[Updated] Click here for our weights used on Synapse.
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[Updated] Click here for our weights used on ACDC. The authors of TransUnet did not provide the split of ACDC dataset. Therefore, we conducted all the ACDC experiments based on our own dataset split.
- Thank you for your interest in our work. We have uploaded the code of our MTUNet to help peers conduct further research on it. However, rest of the codes (such as the training and testing codes) are currently not so well organized, and we plan to release them upon paper publication. It also should be noted that they are still avaliable right now with a rough appearance. Please contact us for these codes if you are new to this field or having difficulty in applying our model to your own dataset.
This is the official implementation for our ICASSP2022 paper MIXED TRANSFORMER UNET FOR MEDICAL IMAGE SEGMENTATION
The entire code will be released upon paper publication.