MICCAI2021 Contest : GAMMA任务一：＜基于多模态眼底影像的青光眼分级＞官方Baseline代码解释

最新推荐文章于 2025-02-04 08:55:38 发布

Alex-Leung

最新推荐文章于 2025-02-04 08:55:38 发布

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分类专栏： paddlepaddle Python 文章标签：计算机视觉 python Paddle

本文链接：https://blog.youkuaiyun.com/a2824256/article/details/119671425

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任务介绍

任务一：基于多模态眼底影像的青光眼分级

在本任务中，我们的目的是分析2D眼底图像和3D OCT扫描体数据两种模态的临床数据，根据视觉特征将样本分级为无青光眼、早期青光眼、中或晚期青光眼三个类别。

官方baseline地址

基于paddle实现的baseline

网络结构

网络的backbone由两个ResNet组成，一个负责提取2D模态图像特征，一个负责提取3D模态图像特征，经过卷积层后，将提取到的多维特征压成一维，通过concat合并为一个一维数组，最后输出分类结果。
网络结构

网络输入

数据格式[batch, channel, height, width]，3D图像使用opencv读入是3通道的，需要转换成单通道灰度图。
数据图像

Code

这里只解释一些非常规的代码，常规代码看paddle官方文档即可

import


import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
# 评估函数
from sklearn.metrics import cohen_kappa_score

import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.vision.models import resnet34
# 这个transforms是一个自行实现的文件，不是第三方库
import transforms as trans

import warnings

DataLoader

dataset类

class GAMMA_sub1_dataset(paddle.io.Dataset):
    """
    getitem() output:
    
    	fundus_img: RGB uint8 image with shape (3, image_size, image_size)
        
        oct_img:    Uint8 image with shape (256, oct_img_size[0], oct_img_size[1])
    """

    def __init__(self,
                 img_transforms,
                 oct_transforms,
                 dataset_root,
                 label_file='',
                 filelists=None,
                 num_classes=3,
                 mode='train'):

        self.dataset_root = dataset_root
        self.img_transforms = img_transforms
        self.oct_transforms = oct_transforms
        self.mode = mode.lower()
        self.num_classes = num_classes
        
        if self.mode == 'train':
            label = {row['data']: row[1:].values 
                        for _, row in pd.read_excel(label_file).iterrows()}

            self.file_list = [[f, label[int(f)]] for f in os.listdir(dataset_root)]
        elif self.mode == "test":
            self.file_list = [[f, None] for f in os.listdir(dataset_root)]
        
        if filelists is not None:
            self.file_list = [item for item in self.file_list if item[0] in filelists]

    def __getitem__(self, idx):
        real_index, label = self.file_list[idx]

        fundus_img_path = os.path.join(self.dataset_root, real_index, real_index + ".jpg")
        # 这里有个问题就是图片读入是乱序的不是按照序号0到255读入
        oct_series_list = sorted(os.listdir(os.path.join(self.dataset_root, real_index, real_index)), 
                                    key=lambda x: int(x.strip("_")[0]))
		
        fundus_img = cv2.imread(fundus_img_path)[:, :, ::-1] # BGR -> RGB
        oct_series_0 = cv2.imread(os.path.join(self.dataset_root, real_index, real_index, oct_series_list[0]), 
                                    cv2.IMREAD_GRAYSCALE)
        oct_img = np.zeros((len(oct_series_list), oct_series_0.shape[0], oct_series_0.shape[1], 1), dtype="uint8")
		
        for k, p in enumerate(oct_series_list):
            oct_img[k] = cv2.imread(
                os.path.join(self.dataset_root, real_index, real_index, p), cv2.IMREAD_GRAYSCALE)[..., np.newaxis]
		# 如果对应图像数据transforms不为空，则对数据进行transforms
        if self.img_transforms is not None:
            fundus_img = self.img_transforms(fundus_img)
        if self.oct_transforms is not None:
            oct_img = self.oct_transforms(oct_img)
 
        # normlize on GPU to save CPU Memory and IO consuming.
        # fundus_img = (fundus_img / 255.).astype("float32")
        # oct_img = (oct_img / 255.).astype("float32")
        
		# 眼底图片改变通道顺序
        fundus_img = fundus_img.transpose(2, 0, 1) # H, W, C -> C, H, W
        # oct图像去掉最后一维
        oct_img = oct_img.squeeze(-1) # D, H, W, 1 -> D, H, W

        if self.mode == 'test':
            return fundus_img, oct_img, real_index
        if self.mode == "train":
            label = label.argmax()
            return fundus_img, oct_img, label

    def __len__(self):
        return len(self.file_list)

进行的transforms

# 训练集眼底图片进行的transforms
img_train_transforms = trans.Compose([
	# 随机resize裁剪
    trans.RandomResizedCrop(
        image_size, scale=(0.90, 1.1), ratio=(0.90, 1.1)),
    # 随机水平翻转
    trans.RandomHorizontalFlip(),
    # 随机垂直翻转
    trans.RandomVerticalFlip(),
    # 随机角度翻转0~30度
    trans.RandomRotation(30)
])
# 训练集oct图片进行的transforms
oct_train_transforms = trans.Compose([
	# 对图片进行中央剪裁
    trans.CenterCrop([256] + oct_img_size),
    trans.RandomHorizontalFlip(),
    trans.RandomVerticalFlip()
])
# 验证集眼底图片进行的transforms
img_val_transforms = trans.Compose([
	# 裁剪中央正方形
    trans.CropCenterSquare(),
    trans.Resize((image_size, image_size))
])
# 验证集oct图片进行的transforms
oct_val_transforms = trans.Compose([
    trans.CenterCrop([256] + oct_img_size)
])

NetWork

网络模型从paddle.vision.models import了resnet34，只要init的时候num_classes设置为0，网络结构就不含有末端全连接层

class Model(nn.Layer):
    """
    simply create a 2-branch network, and concat global pooled feature vector.
    each branch = single resnet34
    """
    def __init__(self):
        super(Model, self).__init__()
        # 带pretrained代表使用paddle的预训练模型，做Transfer Learning
        self.fundus_branch = resnet34(pretrained=True, num_classes=0) # remove final fc
        self.oct_branch = resnet34(pretrained=True, num_classes=0) # remove final fc
        self.decision_branch = nn.Linear(512 * 1 * 2, 3) # ResNet34 use basic block, expansion = 1
        
        # replace first conv layer in oct_branch
        # 对oct提取特征的resnet34的第一层卷积层进行修改，修改通道跟oct图像的输入通道一致，都是256
        self.oct_branch.conv1 = nn.Conv2D(256, 64,
                                        kernel_size=7,
                                        stride=2,
                                        padding=3,
                                        bias_attr=False)
	# 网络组网
    def forward(self, fundus_img, oct_img):
        b1 = self.fundus_branch(fundus_img)
        b2 = self.oct_branch(oct_img)
        # 将图像压成一维
        b1 = paddle.flatten(b1, 1)
        b2 = paddle.flatten(b2, 1)
        # 将两个一维tensor concat在一起，再通过一个全连接层，最后做softmax处理获得分类结果
        logit = self.decision_branch(paddle.concat([b1, b2], 1))

        return logit

评估指标

以下为该任务的评估指标，评估函数使用sklearn库的cohen_kappa_score函数计算

Train & Val

def train(model, iters, train_dataloader, val_dataloader, optimizer, criterion, log_interval, eval_interval):
    iter = 0
    model.train()
    # 储存平均loss和平均kappa的list
    avg_loss_list = []
    avg_kappa_list = []
    best_kappa = 0.
    while iter < iters:
        for data in train_dataloader:
            iter += 1
            if iter > iters:
                break
            # 输入图片int32转float32
            fundus_imgs = (data[0] / 255.).astype("float32")
            oct_imgs = (data[1] / 255.).astype("float32")
            labels = data[2].astype('int64')
			# 模型输入眼底和oct图像
            logits = model(fundus_imgs, oct_imgs)
            # 计算loss
            loss = criterion(logits, labels)
            # acc = paddle.metric.accuracy(input=logits, label=labels.reshape((-1, 1)), k=1)
            for p, l in zip(logits.numpy().argmax(1), labels.numpy()):
                avg_kappa_list.append([p, l])
			
            loss.backward()
            optimizer.step()

            model.clear_gradients()
            avg_loss_list.append(loss.numpy()[0])

            if iter % log_interval == 0:
            	# 计算平均loss, list转ndarray再求均值
                avg_loss = np.array(avg_loss_list).mean()
                # list转ndarray
                avg_kappa_list = np.array(avg_kappa_list)
                # 计算平均kappa
                avg_kappa = cohen_kappa_score(avg_kappa_list[:, 0], avg_kappa_list[:, 1], weights='quadratic')
                # 对两个list进行清空
                avg_loss_list = []
                avg_kappa_list = []
                print("[TRAIN] iter={}/{} avg_loss={:.4f} avg_kappa={:.4f}".format(iter, iters, avg_loss, avg_kappa))

            if iter % eval_interval == 0:
            	# 进行验证操作, 获得验证集的avg_loss和avg_kappa 
                avg_loss, avg_kappa = val(model, val_dataloader, criterion)
                print("[EVAL] iter={}/{} avg_loss={:.4f} kappa={:.4f}".format(iter, iters, avg_loss, avg_kappa))
                # 储存指标最优模型
                if avg_kappa >= best_kappa:
                    best_kappa = avg_kappa
                    paddle.save(model.state_dict(),
                            os.path.join("best_model_{:.4f}".format(best_kappa), 'model.pdparams'))
                model.train()

def val(model, val_dataloader, criterion):
    model.eval()
    avg_loss_list = []
    cache = []
    with paddle.no_grad():
        for data in val_dataloader:
            fundus_imgs = (data[0] / 255.).astype("float32")
            oct_imgs = (data[1] / 255.).astype("float32")
            labels = data[2].astype('int64')
            
            logits = model(fundus_imgs, oct_imgs)
            for p, l in zip(logits.numpy().argmax(1), labels.numpy()):
                cache.append([p, l])

            loss = criterion(logits, labels)
            # acc = paddle.metric.accuracy(input=logits, label=labels.reshape((-1, 1)), k=1)
            avg_loss_list.append(loss.numpy()[0])
    cache = np.array(cache)
    kappa = cohen_kappa_score(cache[:, 0], cache[:, 1], weights='quadratic')
    avg_loss = np.array(avg_loss_list).mean()

    return avg_loss, kappa

训练模型超参数

学习率在baseline是个固定值，batch_size和iteration根据自己实际情况而定，在这里就不写了

Key	Value
优化器	Adam
loss函数	CrossEntropyLoss

其他的一些说明

测试集和验证集的划分

使用的是sklearn的train_test_split函数，进行测试集合验证集的划分

from sklearn.model_selection import train_test_split
val_ratio = 0.2 # 80 / 20
# 省略filelists生成
train_filelists, val_filelists = train_test_split(filelists, test_size=val_ratio, random_state=42)

结果文件生成

# cache为val操作生成的变量
submission_result = pd.DataFrame(cache, columns=['data', 'dense_pred'])
submission_result['non'] = submission_result['dense_pred'].apply(lambda x: int(x[0] == 0))
submission_result['early'] = submission_result['dense_pred'].apply(lambda x: int(x[0] == 1))
submission_result['mid_advanced'] = submission_result['dense_pred'].apply(lambda x: int(x[0] == 2))
# 最后生成提交结果文件
submission_result[['data', 'non', 'early', 'mid_advanced']].to_csv("./submission_sub1.csv", index=False)