DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

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Deep Learning DCSAU-Net
Overview

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

By Qing Xu, Wenting Duan and Na He

Requirements

  1. pytorch==1.10.0
  2. pytorch-lightning==1.1.0
  3. albumentations==0.3.2
  4. seaborn
  5. sklearn

Dataset

To apply the model on a custom dataset, the data tree should be constructed as:

    ├── data
          ├── images
                ├── image_1.png
                ├── image_2.png
                ├── image_n.png
          ├── masks
                ├── image_1.png
                ├── image_2.png
                ├── image_n.png

CSV generation

python data_split_csv.py --dataset your/data/path --size 0.9

Train

python train.py --dataset your/data/path --csvfile your/csv/path --loss dice --batch 16 --lr 0.001 --epoch 150

Evaluation

python eval_binary.py --dataset your/data/path --csvfile your/csv/path --model save_models/epoch_last.pth --debug True

Acknowledgement

The codes are modified from ResNeSt, U-Net

Comments
  • RuntimeError: The size of tensor a (256) must match the size of tensor b (3) at non-singleton dimension 4

    RuntimeError: The size of tensor a (256) must match the size of tensor b (3) at non-singleton dimension 4

    intersection = (inputs * targets).sum() RuntimeError: The size of tensor a (256) must match the size of tensor b (3) at non-singleton dimension 4 您好，我用的是肠息肉数据集。

    image

    input的格式是（2，1，256，256） targets的格式是（2，1，256，256，3） 我的邮箱是[email protected]，QQ是2106639131。 希望您能抽空看一下！十分感谢！

    opened by 1789000076 7
  • dataset

    dataset

    Excuse me, I want to train the ISIC Task 1 official dataset. What should the directory format of the data be? I store the data according to the directory structure of the data you give , and execute the data_ split_ csv，The test machine and training set cannot be divided into csv files .

    opened by Zrs1171757736 5
  • Head of PFC block, 3x3 or 7x7 conv?

    Head of PFC block, 3x3 or 7x7 conv?

    The PFC block code says

    def __init__(self,channels, kernel_size=7):
        super(PFC, self).__init__()
        self.input_layer = nn.Sequential(
                    nn.Conv2d(3, channels, kernel_size, padding= kernel_size // 2),
                    nn.ReLU(inplace=True),
                    nn.BatchNorm2d(channels)

    which implies that the head of the PFC block has a Conv2D with kernel size = 7

    However, in the paper, it is noted that "Also, 3x3 convolution is added to the head of this module for down-sampling the input image and raising the channel because depthwise separable convolution shows degradation of performance on low-dimensional features.

    Should the kernel_size for the input_layer be 3 or 7?

    opened by JohnMBrandt 3
  • RuntimeError: The size of tensor a (256) must match the size of tensor b (3) at non-singleton dimension 4

    RuntimeError: The size of tensor a (256) must match the size of tensor b (3) at non-singleton dimension 4

    请问我该如何解决这个问题，我使用的是Kavisr数据集，代码没有任何改动，但是出现了这个错误 Epoch 0/149

    Traceback (most recent call last): File "D:\paperin\a_new_road\DCSAU-Net-main\train.py", line 176, in model_ft, Loss_list, Accuracy_list = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, File "D:\paperin\a_new_road\DCSAU-Net-main\train.py", line 85, in train_model loss = criterion(outputs, labels) File "D:\ProgramData\Anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "D:\paperin\a_new_road\DCSAU-Net-main\loss.py", line 65, in forward intersection = (inputs * targets).sum()
    RuntimeError: The size of tensor a (256) must match the size of tensor b (3) at non-singleton dimension 4

    这个地方调试的inputs是Tensor(8, 1, 256, 256)，但是targets是Tensor(8, 1, 256, 256, 3),请问是需要修改什么还是我数据集划分的问题 数据集划分 ├── data ├── images ├── cju0qkwl35piu0993l0dewei2.png ....... ├── masks ├── cju0qkwl35piu0993l0dewei2_segmentation.png .......

    opened by MOONWZ 2
  • Could you please share the code of comparison model in the paper

    Could you please share the code of comparison model in the paper

    Thank you for your contribution to relevant work. Could you please share the code of comparison model in the paper, such as ResUnet++ doubleu-net TransUnet levit-unet, which would be very helpful to me

    opened by MOONWZ 2
  • Can you provide the SegPC-2021 dataset you have processed?

    Can you provide the SegPC-2021 dataset you have processed?

    Excellent work！can you provide the labels of the SegPC-2021 dataset you processed? My email is [email protected], I would be very grateful if you can provide it

    opened by jane442 0
Owner
Qing Xu
Qing Xu
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