CHOPPER
CHOPPER
rangz = lambda imagez,blockz: imagez if imagez < blockz else blockz rangz = rangz(imagez,blockz) 貌似这样改就能得出数了~师兄对么？
打扰师兄了，但是这样也会出现新问题，在imagez < blockz的时候，rangz=imagez ，有个病例是imagez =14，这样的话在运行 hr_samples[0, 0:blockz, 0:block_width, 0:block_height] = image[0:rangz, 0:rangwidth, 0:rangheight] 时就会报错，报错内容：无法将（14,96,96）转换为（16,96,96），师兄是不是缺少这样情况的处理呀？您之前版本的data3dprepare.py在这方面貌似是做了判断的~打扰您了~谢谢~
您好，师兄，麻烦问您一下，vnet3d_predict.py测试程序的输入图像数据是data3dprepare.py处理后的数据嘛？如果是这样的话，那就相当于是：在已知结节质心位置进行分割了呀？但是测试新病例我们是不知道结节位置在哪里的呀~是不是我理解上有偏差？
好的，收到~多谢师兄~
> Hi~ Thanks for your question. > I am not sure if I truly understand your question...But I could try to give a pipeline of face recognition as below: >...
> Thanks for your attention to our work. The code is now available at https://github.com/jianpengz/DoDNet, which would be forked to this site later. Got it, thanks ~~~
Thanks for your reply. I have read the paper four times, but found no training steps, can you help me with the running steps? I have tried the following steps:...
> Dear all, > > We have uploaded the pretrained weight: [here](https://drive.google.com/file/d/1hxV8nnB8lkCGsOhjJNxM02LqQuL6WP9D/view?usp=drive_link). > > Alternatively, you could pretrain the weight as the first stage use only Dice loss by yourselves....