医学图像算法之基于Unet++的息肉分割
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医学图像算法之基于Unet++的息肉分割
** 第一步：准备数据**
息肉分割数据，总共有1000张
第二步：搭建模型
UNet++，这是一种旨在克服以上限制的新型通用图像分割体系结构。如下图所示，UNet++由不同深度的U-Net组成，其解码器通过重新设计的跳接以相同的分辨率密集连接。 UNet++中引入的体系结构更改具有以下优点。首先，UNet++不易明确地选择网络深度，因为它在其体系结构中嵌入了不同深度的U-Net 。所有这些U-Net都部分共享一个编码器，而它们的解码器则交织在一起。通过在深度监督下训练UNet++，可以同时训练所有组成的U-Net，同时受益于共享的图像表示。这种设计不仅可以提高整体分割性能，而且可以在推理期间修剪模型。其次，UNet++不会受到不必要的限制性跳接的限制，在这种情况下，只能融合来自编码器和解码器的相同比例的特征图 。UNet++中引入的经过重新设计的跳接在解码器节点处提供了不同比例的特征图，从而使聚合层可以决定如何将跳接中携带的各种特征图与解码器特征图融合在一起。通过以相同的分辨率密集连接组成部分U-Net的解码器，可以在UNet++中实现重新设计的跳接。作者在六个分割数据集和不同深度的多个主干中对UNet++进行了广泛地评估：
🔥计算机视觉、图像处理、毕业辅导、作业帮助、代码获取，远程协助，代码定制，私聊会回复!
✍🏻作者简介：机器学习，深度学习，卷积神经网络处理，图像处理
🚀B站项目实战：https://space.bilibili.com/364224477
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🤵♂代做需求：@个人主页
五个贡献：
第三步：代码
1）损失函数为：交叉熵损失函数+dice_loss
2）网络代码：
class UnetPlusPlus(nn.Module):
def __init__(self, num_classes, deep_supervision=False):
super(UnetPlusPlus, self).__init__()
self.num_classes = num_classes
self.deep_supervision = deep_supervision
self.filters = [64, 128, 256, 512, 1024]
self.CONV3_1 = ContinusParalleConv(512 * 2, 512, pre_Batch_Norm=True)
self.CONV2_2 = ContinusParalleConv(256 * 3, 256, pre_Batch_Norm=True)
self.CONV2_1 = ContinusParalleConv(256 * 2, 256, pre_Batch_Norm=True)
self.CONV1_1 = ContinusParalleConv(128 * 2, 128, pre_Batch_Norm=True)
self.CONV1_2 = ContinusParalleConv(128 * 3, 128, pre_Batch_Norm=True)
self.CONV1_3 = ContinusParalleConv(128 * 4, 128, pre_Batch_Norm=True)
self.CONV0_1 = ContinusParalleConv(64 * 2, 64, pre_Batch_Norm=True)
self.CONV0_2 = ContinusParalleConv(64 * 3, 64, pre_Batch_Norm=True)
self.CONV0_3 = ContinusParalleConv(64 * 4, 64, pre_Batch_Norm=True)
self.CONV0_4 = ContinusParalleConv(64 * 5, 64, pre_Batch_Norm=True)
self.stage_0 = ContinusParalleConv(3, 64, pre_Batch_Norm=False)
self.stage_1 = ContinusParalleConv(64, 128, pre_Batch_Norm=False)
self.stage_2 = ContinusParalleConv(128, 256, pre_Batch_Norm=False)
self.stage_3 = ContinusParalleConv(256, 512, pre_Batch_Norm=False)
self.stage_4 = ContinusParalleConv(512, 1024, pre_Batch_Norm=False)
self.pool = nn.MaxPool2d(2)
self.upsample_3_1 = nn.ConvTranspose2d(in_channels=1024, out_channels=512, kernel_size=4, stride=2, padding=1)
self.upsample_2_1 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=4, stride=2, padding=1)
self.upsample_2_2 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=4, stride=2, padding=1)
self.upsample_1_1 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1)
self.upsample_1_2 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1)
self.upsample_1_3 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1)
self.upsample_0_1 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1)
self.upsample_0_2 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1)
self.upsample_0_3 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1)
self.upsample_0_4 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1)
# 分割头
self.final_super_0_1 = nn.Sequential(
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, self.num_classes, 3, padding=1),
)
self.final_super_0_2 = nn.Sequential(
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, self.num_classes, 3, padding=1),
)
self.final_super_0_3 = nn.Sequential(
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, self.num_classes, 3, padding=1),
)
self.final_super_0_4 = nn.Sequential(
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, self.num_classes, 3, padding=1),
)
def forward(self, x):
x_0_0 = self.stage_0(x)
x_1_0 = self.stage_1(self.pool(x_0_0))
x_2_0 = self.stage_2(self.pool(x_1_0))
x_3_0 = self.stage_3(self.pool(x_2_0))
x_4_0 = self.stage_4(self.pool(x_3_0))
x_0_1 = torch.cat([self.upsample_0_1(x_1_0), x_0_0], 1)
x_0_1 = self.CONV0_1(x_0_1)
x_1_1 = torch.cat([self.upsample_1_1(x_2_0), x_1_0], 1)
x_1_1 = self.CONV1_1(x_1_1)
x_2_1 = torch.cat([self.upsample_2_1(x_3_0), x_2_0], 1)
x_2_1 = self.CONV2_1(x_2_1)
x_3_1 = torch.cat([self.upsample_3_1(x_4_0), x_3_0], 1)
x_3_1 = self.CONV3_1(x_3_1)
x_2_2 = torch.cat([self.upsample_2_2(x_3_1), x_2_0, x_2_1], 1)
x_2_2 = self.CONV2_2(x_2_2)
x_1_2 = torch.cat([self.upsample_1_2(x_2_1), x_1_0, x_1_1], 1)
x_1_2 = self.CONV1_2(x_1_2)
x_1_3 = torch.cat([self.upsample_1_3(x_2_2), x_1_0, x_1_1, x_1_2], 1)
x_1_3 = self.CONV1_3(x_1_3)
x_0_2 = torch.cat([self.upsample_0_2(x_1_1), x_0_0, x_0_1], 1)
x_0_2 = self.CONV0_2(x_0_2)
x_0_3 = torch.cat([self.upsample_0_3(x_1_2), x_0_0, x_0_1, x_0_2], 1)
x_0_3 = self.CONV0_3(x_0_3)
x_0_4 = torch.cat([self.upsample_0_4(x_1_3), x_0_0, x_0_1, x_0_2, x_0_3], 1)
x_0_4 = self.CONV0_4(x_0_4)
if self.deep_supervision:
out_put1 = self.final_super_0_1(x_0_1)
out_put2 = self.final_super_0_2(x_0_2)
out_put3 = self.final_super_0_3(x_0_3)
out_put4 = self.final_super_0_4(x_0_4)
return [out_put1, out_put2, out_put3, out_put4]
else:
return self.final_super_0_4(x_0_4)
第四步：统计一些指标（训练过程中的loss和miou）
第五步：搭建GUI界面
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有训练代码和训练好的模型以及训练过程，提供数据，提供GUI界面代码
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