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bi-unet's Introduction

Bi-UNET

Try to use the more lightweight BiFormer's attention mechanism combined with the UNet network to build a Bi-unet medical segmentation model.

The attention mechanism comes from: 2023 CVPR BiFormer: Vision Transformer with Bi-Level Routing Attention Their code in :https://github.com/rayleizhu/BiFormer paper:Zhu L, Wang X, Ke Z, et al. BiFormer: Vision Transformer with Bi-Level Routing Attention[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 10323-10333.

AND For segmented network architecture, we use the most classic :Swin-unet

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bi-unet's Issues

作者你好,似乎你并没有做最后一步上采样?

    model = Bi_Unet().to(torch.device('cuda:0'))
    input = torch.randn(1, 3, 512, 512)  # 创建输入张量
    output = model(input)  # 输入模型得到输出
    print(output.shape)  #  torch.Size([1, 64, 128, 128])

如果是做医学影像之类的二分割任务,请问这最后得到的[1, 64, 128, 128]张量如何处理最佳

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