\ub525\ub7ec\ub2dd \ubaa8\ub378 \uc911 \ud558\ub098\uc778 U-Net\uc740 \uc8fc\ub85c \uc758\ub8cc \uc774\ubbf8\uc9c0 \ubd84\ud560\uc5d0\uc11c \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4. U-Net \ubaa8\ub378\uc740 \uc774\ubbf8\uc9c0\ub97c \ud53d\uc140 \ub2e8\uc704\ub85c \ubd84\ud560\ud574\uc57c \ud558\ub294 \uc791\uc5c5\uc5d0 \ud2b9\ud788 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4. \uc774 \ube14\ub85c\uadf8 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 U-Net\uc758 \uac1c\ub150, \uad6c\uc870, \uadf8\ub9ac\uace0 \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0c1\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
U-Net\uc740 2015\ub144 Olaf Ronneberger, Philipp Fischer, Thomas Becker\uc5d0 \uc758\ud574 \uc81c\uc548\ub41c \ubaa8\ub378\ub85c, \uc758\ub8cc \uc774\ubbf8\uc9d5 \ub300\ud68c\uc778 ISBI\uc5d0\uc11c \uc6b0\uc218\ud55c \uc131\ub2a5\uc744 \ubcf4\uc600\uc2b5\ub2c8\ub2e4. U-Net\uc740 \uc77c\ubc18\uc801\uc778 Convolutional Neural Network (CNN)\uc758 \uc544\ud0a4\ud14d\ucc98\uc5d0\uc11c \ucd9c\ubc1c\ud558\uc5ec, \ud2b9\uc9d5 \ucd94\ucd9c \ubc0f \uc138\uadf8\uba58\ud14c\uc774\uc158 \uc791\uc5c5\uc744 \ub3d9\uc2dc\uc5d0 \uc218\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc774\uc720\ub85c U-Net\uc740 \ud2b9\uc218\ud55c \uc138\uadf8\uba58\ud14c\uc774\uc158 \uc791\uc5c5\uc5d0\uc11c \ub192\uc740 \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4.<\/p>\n
U-Net\uc758 \uad6c\uc870\ub294 \ud06c\uac8c \ub450 \ubd80\ubd84\uc73c\ub85c \ub098\ub269\ub2c8\ub2e4: \ub2e4\uc6b4\uc0d8\ud50c\ub9c1(\ud074\ub9ac\ub2dd) \uacbd\ub85c\uc640 \uc5c5\uc0d8\ud50c\ub9c1(\ud655\uc7a5) \uacbd\ub85c\uc785\ub2c8\ub2e4. \ub2e4\uc6b4\uc0d8\ud50c\ub9c1 \uacbd\ub85c\ub294 \uc774\ubbf8\uc9c0\ub97c \uc810\ucc28\uc801\uc73c\ub85c \uc904\uc774\uba74\uc11c \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\uace0, \uc5c5\uc0d8\ud50c\ub9c1 \uacbd\ub85c\ub294 \uc810\ucc28\uc801\uc73c\ub85c \uc774\ubbf8\uc9c0\ub97c \ubcf5\uc6d0\ud558\uba74\uc11c \uc138\uadf8\uba58\ud14c\uc774\uc158 \ub9f5\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
\ub2e4\uc6b4\uc0d8\ud50c\ub9c1 \uacbd\ub85c\ub294 \uc5ec\ub7ec \uac1c\uc758 Convolutional \ube14\ub85d\uc73c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \ube14\ub85d\uc740 Convolutional \ub808\uc774\uc5b4\uc640 \ud65c\uc131\ud654 \ud568\uc218, \ud480\ub9c1 \ub808\uc774\uc5b4\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc774\ub807\uac8c \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uba74\uc11c \uc774\ubbf8\uc9c0\uc758 \ud06c\uae30\uac00 \uc904\uc5b4\ub4e4\uace0, \ud2b9\uc9d5\uc774 \ub354\uc6b1 \uac15\uc870\ub429\ub2c8\ub2e4.<\/p>\n
\uc5c5\uc0d8\ud50c\ub9c1 \uacbd\ub85c\uc5d0\uc11c\ub294 \uc5c5\uc0d8\ud50c\ub9c1 \ub808\uc774\uc5b4\ub97c \ud1b5\ud574 \uc774\ubbf8\uc9c0\ub97c \uc6d0\ub798 \ud06c\uae30\ub85c \ubcf5\uc6d0\ud558\ub294 \uacfc\uc815\uc774 \uc9c4\ud589\ub429\ub2c8\ub2e4. \uc774\ub54c, \ub2e4\uc6b4\uc0d8\ud50c\ub9c1 \uacbd\ub85c\uc5d0\uc11c \ucd94\ucd9c\ub41c \ud2b9\uc9d5\ub4e4\uacfc \ubcd1\ud569\ud558\uc5ec \uc138\ubd84\ud654\ub41c \uc815\ubcf4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uac01 \ud53d\uc140\uc5d0 \ub300\ud55c \uc608\uce21 \uc815\ud655\ub3c4\ub97c \ub192\uc785\ub2c8\ub2e4.<\/p>\n
U-Net\uc740 ‘Skip Connections’\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc6b4\uc0d8\ud50c\ub9c1 \uacbd\ub85c\uc640 \uc5c5\uc0d8\ud50c\ub9c1 \uacbd\ub85c\uc5d0\uc11c\uc758 \ub370\uc774\ud130\ub97c \uc5f0\uacb0\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc815\ubcf4 \uc190\uc2e4\uc744 \ucd5c\uc18c\ud654\ud558\uace0, \ub354\uc6b1 \uc815\uad50\ud55c \uc138\uadf8\uba58\ud14c\uc774\uc158 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774\uc81c PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec U-Net \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uba3c\uc800 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud558\uace0 \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4.<\/p>\n
\n \n # \ud544\uc694\ud55c \ud328\ud0a4\uc9c0 \uc784\ud3ec\ud2b8\n import torch\n import torch.nn as nn\n import torch.nn.functional as F\n from torchvision import transforms\n from torchvision import datasets\n from torch.utils.data import DataLoader\n <\/code>\n <\/pre>\n3.1 U-Net \ubaa8\ub378 \uc815\uc758<\/h3>\n
\uc544\ub798\ub294 U-Net \ubaa8\ub378\uc758 \uae30\ubcf8 \uad6c\uc870\ub97c \uc815\uc758\ud55c \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n
\n \n class UNet(nn.Module):\n def __init__(self, in_channels, out_channels):\n super(UNet, self).__init__()\n\n self.encoder1 = self.conv_block(in_channels, 64)\n self.encoder2 = self.conv_block(64, 128)\n self.encoder3 = self.conv_block(128, 256)\n self.encoder4 = self.conv_block(256, 512)\n\n self.bottom = self.conv_block(512, 1024)\n\n self.decoder4 = self.upconv_block(1024, 512)\n self.decoder3 = self.upconv_block(512, 256)\n self.decoder2 = self.upconv_block(256, 128)\n self.decoder1 = self.upconv_block(128, 64)\n\n self.final_conv = nn.Conv2d(64, out_channels, kernel_size=1)\n\n def conv_block(self, in_channels, out_channels):\n return nn.Sequential(\n nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n nn.ReLU(inplace=True)\n )\n\n def upconv_block(self, in_channels, out_channels):\n return nn.Sequential(\n nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2),\n nn.ReLU(inplace=True)\n )\n\n def forward(self, x):\n enc1 = self.encoder1(x)\n enc2 = self.encoder2(F.max_pool2d(enc1, kernel_size=2))\n enc3 = self.encoder3(F.max_pool2d(enc2, kernel_size=2))\n enc4 = self.encoder4(F.max_pool2d(enc3, kernel_size=2))\n\n bottleneck = self.bottom(F.max_pool2d(enc4, kernel_size=2))\n\n dec4 = self.decoder4(bottleneck)\n dec4 = torch.cat((dec4, enc4), dim=1)\n dec4 = self.conv_block(dec4.size(1), dec4.size(1))(dec4)\n\n dec3 = self.decoder3(dec4)\n dec3 = torch.cat((dec3, enc3), dim=1)\n dec3 = self.conv_block(dec3.size(1), dec3.size(1))(dec3)\n\n dec2 = self.decoder2(dec3)\n dec2 = torch.cat((dec2, enc2), dim=1)\n dec2 = self.conv_block(dec2.size(1), dec2.size(1))(dec2)\n\n dec1 = self.decoder1(dec2)\n dec1 = torch.cat((dec1, enc1), dim=1)\n dec1 = self.conv_block(dec1.size(1), dec1.size(1))(dec1)\n\n return self.final_conv(dec1)\n <\/code>\n <\/pre>\n3.2 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\uc774\uc81c U-Net \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \uc900\ube44\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc9c0\uc815\ud558\uace0, \ud559\uc2b5 \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4.<\/p>\n
\n \n # \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc815\uc758\n num_epochs = 25\n learning_rate = 0.001\n\n # \ubaa8\ub378 \uc0dd\uc131\n model = UNet(in_channels=3, out_channels=1).cuda()\n criterion = nn.BCEWithLogitsLoss()\n optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n\n # \ub370\uc774\ud130 \ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac\n transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Resize((128, 128)),\n ])\n\n train_dataset = datasets.ImageFolder(root='your_dataset_path\/train', transform=transform)\n train_loader = DataLoader(dataset=train_dataset, batch_size=16, shuffle=True)\n\n # \ubaa8\ub378 \ud559\uc2b5\n for epoch in range(num_epochs):\n for images, masks in train_loader:\n images = images.cuda()\n masks = masks.cuda()\n\n # \uc21c\uc804\ud30c\n outputs = model(images)\n loss = criterion(outputs, masks)\n\n # \uc5ed\uc804\ud30c \ubc0f \ucd5c\uc801\ud654\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n print(f'Epoch [{epoch+1}\/{num_epochs}], Loss: {loss.item():.4f}')\n <\/code>\n <\/pre>\n4. U-Net\uc758 \ud65c\uc6a9 \uc0ac\ub840<\/h2>\n
U-Net\uc740 \uc8fc\ub85c \uc758\ub8cc \uc774\ubbf8\uc9c0 \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub418\uc9c0\ub9cc, \uadf8 \uc678\uc5d0\ub3c4 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4:<\/p>\n
\n- \uc758\ub8cc \uc774\ubbf8\uc9c0 \ubd84\uc11d:<\/strong> CT \uc2a4\uce94, MRI \uc774\ubbf8\uc9c0 \ubd84\ud560 \ub4f1\uc5d0\uc11c \uc870\uc9c1, \uc885\uc591 \ub4f1\uc744 \uc815\ud655\ud788 \uc2dd\ubcc4.<\/li>\n
- \uc704\uc131 \uc774\ubbf8\uc9c0 \ubd84\uc11d:<\/strong> \uc9c0\ud615 \ubd84\ud560, \ub3c4\uc2dc \uacc4\ud68d \ub4f1.<\/li>\n
- \uc790\uc728\uc8fc\ud589\ucc28:<\/strong> \ub3c4\ub85c, \uc7a5\uc560\ubb3c \uac80\ucd9c \ub4f1.<\/li>\n
- \ube44\ub514\uc624 \ucc98\ub9ac:<\/strong> \ud2b9\uc815 \uac1d\uccb4 \ucd94\uc801, \ud589\ub3d9 \uc778\uc2dd \ub4f1.<\/li>\n<\/ul>\n
5. \uacb0\ub860<\/h2>\n
U-Net\uc740 \uadf8 \uad6c\uc870 \ub355\ubd84\uc5d0 \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \ubd84\ud560 \uc791\uc5c5\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 U-Net\uc758 \uae30\ucd08\ubd80\ud130 \uad6c\ud604\uc5d0 \uc774\ub974\uae30\uae4c\uc9c0 \ub2e4\ub8e8\uc5b4 \ubcf4\uc558\uc2b5\ub2c8\ub2e4. U-Net\uc740 \ud2b9\ud788 \uc758\ub8cc \uc601\uc0c1 \ubd84\uc57c\uc5d0\uc11c \ub110\ub9ac \uc0ac\uc6a9\ub418\uc9c0\ub9cc, \uadf8 \uc751\uc6a9\uc740 \uadf8 \ubc94\uc704\ub97c \ud6e8\uc52c \ub6f0\uc5b4\ub118\uc2b5\ub2c8\ub2e4. \ud604\uc7ac\uc758 \ub525\ub7ec\ub2dd \uae30\uc220\uc774 \ub354\uc6b1 \ubc1c\uc804\ud568\uc5d0 \ub530\ub77c, U-Net\uc758 \ub2e4\uc591\ud55c \ubcc0\ud615 \ubc0f \uac19\uc740 \ub124\ud2b8\uc6cc\ud06c \uad6c\uc870\ub97c \ud65c\uc6a9\ud55c \uc0c8\ub85c\uc6b4 \uc811\uadfc \ubc29\uc2dd\uc774 \uae30\ub300\ub429\ub2c8\ub2e4.<\/p>\n
\ucc38\uace0 \uc790\ub8cc<\/h2>\n\n- Ronneberger, Olaf, et al. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” Medical Image Computing and Computer-Assisted Intervention. 2015.<\/li>\n
- Pytorch Documentation: https:\/\/pytorch.org\/docs\/stable\/index.html<\/a><\/li>\n<\/ul>\n
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