kidney_pathology_AI

This is the code for Our KI paper: Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy, which mainly focusing on glomeruli classification and cell/mesangial area segmentation tasks.

1. Introduction about the glomeruli classification network - efficientnet

   EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models. We develop EfficientNets based on AutoML and Compound Scaling. In particular, we first use AutoML Mobile framework to develop a mobile-size baseline network, named as EfficientNet-B0; Then, we use the compound scaling method to scale up this baseline to obtain EfficientNet-B1 to B7.

   1.1 Load a pretrained EfficientNet:

    from efficientnet_pytorch import EfficientNet
    model = EfficientNet.from_pretrained('efficientnet-b0')
   

   1.2 Example: how to build a pipeline for classification

    Below is a simple, complete example.
   
    We assume that in your current directory, there is a img.jpg file and a labels_map.txt file (ImageNet class names). These are both included in examples/simple.
   
    import json
    from PIL import Image
    import torch
    from torchvision import transforms
   
    from efficientnet_pytorch import EfficientNet
    model = EfficientNet.from_pretrained('efficientnet-b0')
   
    # Preprocess image
    tfms = transforms.Compose([transforms.Resize(224), transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),])
    img = tfms(Image.open('img.jpg')).unsqueeze(0)
    print(img.shape) # torch.Size([1, 3, 224, 224])
   
    # Load ImageNet class names
    labels_map = json.load(open('labels_map.txt'))
    labels_map = [labels_map[str(i)] for i in range(1000)]
   
    # Classify
    model.eval()
    with torch.no_grad():
        outputs = model(img)
   
    # Print predictions
    print('-----')
    for idx in torch.topk(outputs, k=5).indices.squeeze(0).tolist():
        prob = torch.softmax(outputs, dim=1)[0, idx].item()
        print('{label:<75} ({p:.2f}%)'.format(label=labels_map[idx], p=prob*100))
   

2. Introduction about the Cell/Mesangial Area Segmentation network - UNet we tested U-Net, R2U-Net, Attention U-Net, Attention R2U-Net on Celland Mesangial Area Segmentation, refered to https://github.com/LeeJunHyun/Image_Segmentation implementation. And found that the Attention U-Net is the most suitable network for our specfic task. The readers can refer to the following papers for details. U-Net: Convolutional Networks for Biomedical Image Segmentation

   https://arxiv.org/abs/1505.04597

   Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

   https://arxiv.org/abs/1802.06955

   Attention U-Net: Learning Where to Look for the Pancreas

   https://arxiv.org/abs/1804.03999

   Attention R2U-Net : Just integration of two recent advanced works (R2U-Net + Attention U-Net)

3. Introduction about the Glomeruli Segmentation network - VNet we implemented various vnet varients to tackling the glomeruli segmentation task. In the vnet folder, you can see 2.5d、3、fine_grained vnet network architecture, and different backbones used for vnet, and many losses used during training the network. The readers can easyily combine these together. We used keras framework to implement these things.