true positive(TP):预测正确, 预测结果是正类, 真实是正类 false positive(FP):预测错误, 预测结果是正类, 真实是负类 true negative(TN):预测正确, 预测结果是负类, 真实是负类 false negative(FN):预测错误, 预测结果是负类, 真实是正类 2.mIoU定义与单个IoU理解 计算真实值和预测值两个集合的交集和并...
TN: true negative,真阴性,预测是阴性,预测对了,实际也是负例。 FP: false positive,假阳性,预测是阳性,预测错了,实际是负例。 FN: false negative,假阴性,预测是阴性,预测错了,实际是正例。 这里dice coefficient可以写成如下形式: dice=2TP2TP+FP+FN 而我们知道: precision=TPTP+FP,recall=TPTP+FN F1...
squared_pred: 是否对模型输出进行平方操作,默认为True。 to_onehot_y: 是否将标签转换为one-hot编码形式,默认为False。 other actiavtion: 可选的额外激活函数,例如softmax。 通过合理选择和调整这些参数,可以根据不同的数据集和任务获得更好的训练效果。 总结: MONAI DiceLoss函数是一个强大的工具,用于优化深度...
如果我们令A是所有模型预测为正的样本的集合,令B为所有实际上为正类的样本集合,那么DSC就可以重写为: 其中,TP是True Positive,FN是False Negative,FP是False Negative,D是数据集,f是一个分类模型。于是,在这个意义上,DSC是和F1等价的。 既然如此,我们就想直接优化DSC,然而上述表达式是离散的。为此,我们需要把上...
nn.functional as F def dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon=1e-6): # Average of Dice coefficient for all batches, or for a single mask assert input.size() == target.size() if input.dim() == 2 and reduce_batch_first: raise ...
reduce=False) loss_kernels.append(loss_kernel_i) loss_kernels = torch.mean(torch.stack(loss_kernels, dim=1), dim=1) iou_kernel = iou( (kernels[:, -1, :, :] > 0).long(), gt_kernels[:, -1, :, :], training_masks * gt_texts, reduce=False) losses.update(dict( loss_kernels...
naive_dice:Union[bool,None]=False, avg_factor:Union[int,None]=None, ignore_index:Union[int,None]=255)->float: """Calculate dice loss, there are two forms of dice loss is supported: - the one proposed in `V-Net: Fully Convolutional Neural ...
gamma =2alpha =0.25'''tf.where(tensor,a,b):将tensor中true位置元素替换为a中对应位置元素,false的替换为b中对应位置元素'''pt_1 = tf.where(tf.equal(y_true,1), y_pred, tf.ones_like(y_pred)) pt_0 = tf.where(tf.equal(y_true,0), y_pred, tf.zeros_like(y_pred)) ...
def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False): #num_iterations-梯度下降次数 learning_rate-学习率,即参数ɑ w, b = initialize_with_zeros(X_train.shape[0]) #初始化参数w,b ...
Dice loss is based on the Sorensen-Dice coefficient (Sorensen, 1948) or Tversky index (Tversky, 1977), which attaches similar importance to false positives and false negatives, and is more immune to the data-imbalance issue. To further alleviate the dominating influence from easy-negative ...