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View Code? Open in Web Editor NEW[MICCAI 2021] BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation
Home Page: https://bionets.github.io/
[MICCAI 2021] BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation
Home Page: https://bionets.github.io/
Dear author:
Your team has made great contributions to the development of medical image segmentation, such as BiO-Net, BiX-NAS and so on.
I have a question about the BiO-Net++. BiO-Net++ adds a lot of dense connections between encode blocks and decode blocks on the basis of BiO-Net. Why is the number of parameters two orders of magnitude less than that of BiO-Net?
Hope to get your answer, thank you very much.
Is the number of out_channel in each block the same
Thanks for your extraordinary work !
At Phase2, it goes well when 15 subnetworks are sampled at iteration 5.
But when it goes to interation 4, the subnetworks tripled as 3 networks in pareto front are obtained in previous iter.
45 subnetworks causes an OOM error in a single NVIDIA GeForce RTX 2080 Ti (11GB) in my case.
How much GPU memory does it run the whole experiment pipeline ?
I am able to reproduce the MoNuSeg mIoU and DICE for phase1 search. Parameters are the same, but the reported MACs in the training log seems 15-20x as the paper shows. It's because MACs are caculated with a smaller input size (resolution) to align with other works ?
best,
Below Replace with the IAA augmented method
transform = torchvision.transforms.Compose([
transforms.Resize((224, 224)),
transforms.ColorJitter(hue=.05, saturation=.05),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(20, resample=PIL.Image.BILINEAR),
transforms.ToTensor()
])
x, y = self.transforms(images=self.x[None, index], segmentation_maps=self.y[None, index])
TypeError: call() got an unexpected keyword argument 'images'
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