问题1：'Keyword argument not understood:', 'input
删去input=，output=
论文的几个创新点：数据增强，
论文用的一些方法：数据增强，数据的二值化
神经网络的训练：
遇到的一些问题：1、读取数据时一开始用libtiff读取tif格式图片一直导入不成功，后来更换成cv.imgread
2.tensorflow 调用adam函数时出现错误，通过查阅资料得知是Adam.keras版本需要匹配。
网络的版本号
主要结构：一个语义分割模型，encoder-decoder结构，u字形（论文中的输入大小是512*512，但这副图里给的是572*572，图片数据经过处理
特点：U型结构和skip-connection
蓝/白色框表示 feature map；蓝色箭头表示 3x3 卷积，用于特征提取；灰色箭头表示 skip-connection，用于特征融合；红色箭头表示池化 pooling，用于降低维度；绿色箭头表示上采样 upsample，用于恢复维度；青色箭头表示 1x1 卷积，用于输出结果。
UNet的encoder下采样4次，一共下采样16倍，对称地，其decoder也相应上采样4次，将encoder得到的高级语义特征图恢复到原图片的分辨率。
Skip connection：打破了网络的对称性，提升了网络的表征能力，关于对称性引发的特征退化问题 残差连接（skip connect）/（residual connections）_赵凯月的博客-CSDN博客
医疗影像有什么样的特点：图像语义较为简单、结构较为固定。我们做脑的，就用脑CT和脑MRI，做胸片的只用胸片CT，做眼底的只用眼底OCT，都是一个固定的器官的成像，而不是全身的。由于器官本身结构固定和语义信息没有特别丰富，所以高级语义信息和低级特征都显得很重要(UNet的skip connection（残差连接）和U型结构就派上了用场)。举两个例子直观感受下。
U-net 基于pytorch的代码：
import torch
import torch.nn as nn
import torch.nn.functional as F
class double_conv2d_bn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, strides=1, padding=1):
初始化网络
super(double_conv2d_bn, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels,
kernel_size=kernel_size,
stride=strides, padding=padding, bias=True)
self.conv2 = nn.Conv2d(out_channels, out_channels,
kernel_size=kernel_size,
stride=strides, padding=padding, bias=True)
self.bn1 = nn.BatchNorm2d(out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
return out
class deconv2d_bn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, strides=2):
super(deconv2d_bn, self).__init__()
self.conv1 = nn.ConvTranspose2d(in_channels, out_channels,
kernel_size=kernel_size,
stride=strides, bias=True)
self.bn1 = nn.BatchNorm2d(out_channels)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
return out
class Unet(nn.Module):
def __init__(self):
super(Unet, self).__init__()
self.layer1_conv = double_conv2d_bn(1, 8)
self.layer2_conv = double_conv2d_bn(8, 16)
self.layer3_conv = double_conv2d_bn(16, 32)
self.layer4_conv = double_conv2d_bn(32, 64)
self.layer5_conv = double_conv2d_bn(64, 128)
self.layer6_conv = double_conv2d_bn(128, 64)
self.layer7_conv = double_conv2d_bn(64, 32)
self.layer8_conv = double_conv2d_bn(32, 16)
self.layer9_conv = double_conv2d_bn(16, 8)
self.layer10_conv = nn.Conv2d(8, 1, kernel_size=3,
stride=1, padding=1, bias=True)
self.deconv1 = deconv2d_bn(128, 64)
self.deconv2 = deconv2d_bn(64, 32)
self.deconv3 = deconv2d_bn(32, 16)
self.deconv4 = deconv2d_bn(16, 8)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
conv1 = self.layer1_conv(x)
pool1 = F.max_pool2d(conv1, 2)
conv2 = self.layer2_conv(pool1)
pool2 = F.max_pool2d(conv2, 2)
conv3 = self.layer3_conv(pool2)
pool3 = F.max_pool2d(conv3, 2)
conv4 = self.layer4_conv(pool3)
pool4 = F.max_pool2d(conv4, 2)
conv5 = self.layer5_conv(pool4)
convt1 = self.deconv1(conv5)
concat1 = torch.cat([convt1, conv4], dim=1)
conv6 = self.layer6_conv(concat1)
convt2 = self.deconv2(conv6)
concat2 = torch.cat([convt2, conv3], dim=1)
conv7 = self.layer7_conv(concat2)
convt3 = self.deconv3(conv7)
concat3 = torch.cat([convt3, conv2], dim=1)
conv8 = self.layer8_conv(concat3)
convt4 = self.deconv4(conv8)
concat4 = torch.cat([convt4, conv1], dim=1)
conv9 = self.layer9_conv(concat4)
outp = self.layer10_conv(conv9)
outp = self.sigmoid(outp)
return outp
model = Unet()
inp = torch.rand(10, 1, 224, 224)
outp = model(inp)
print(outp.shape)