Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function - xialab-ahu/ETFC
The primary function is calculating the ablation experiment’s loss function on the Cityspace dataset. The experimental results are represented by lines and values, as shown in Fig. 9. Dice loss plus lce improves the segmentation results of “car” compared to only cross-entropy loss lce. It’...
The combined loss function is defined as follows: Ltotal=λLCE+(1−λ)LDice (3) where λ is a weight hyperparameter and fixed to 0.4, as determined based on [14, 30]. Cross perceptron To effectively integrate multi-scale information, we propose the Cross Perceptron for feature fusion ...
The authors investigate the behavior of Dice loss, cross-entropy loss and generalized dice loss functions in the presence of different rates of label imbalance across 2D and 3D segmentation tasks. The results demonstrate that the GDL is more robust than the other loss functions(4)LossGDL=1−2...
applied a weighted ce loss for abdomen multi-organ segmentation. dice loss milletari et al . [ 90 ] proposed the dice loss to quantify the intersection between volumes, which converted the voxel-based measure to a semantic label overlap measure, becoming a commonly used loss function in ...
23 introduced the Bi-tempered loss function. They propose two changes to the default softmax categorical cross-entropy. First they replace the softmax output with a heavy tailed softmax, that acts as a form of ’label smoothing’. The replacement softmax function is given by: y^i=expt2...
支持BERT、ERNIE、ROBERTA、NEZHA、ALBERT、XLNET、ELECTRA、GPT-2、TinyBERT、XLM、T5等预训练模型; 支持BCE-Loss、Focal-Loss、Circle-Loss、Prior-Loss、Dice-Loss、LabelSmoothing等损失函数; 具有依赖轻量、代码简洁、注释详细、调试清晰、配置灵活、拓展方便、适配NLP等特性。 目录 安装 数据 使用方式 paper 参考...
The Pott’s Model is commonly used as the label compatibility function, giving μ(zi,zj)=[zi≠zj]. The corresponding energy penalty is given by the function k, which is defined over an arbitrary feature space, with fi, fj being the feature vectors of the pair of voxels. Krähenbühl ...
We used a soft Dice score as a loss function. Let Lc=(l0c,l1c,…,lMc)be ground truth label image (l0c) and M represents different scales for multi-scale HF. The c represents the type either HF or BG. Then, let Sc=(s0c,s1c,…,sMc) be the segmentation resulting from the ...
Our proposed loss function is a combination of BCE Loss, Focal Loss, and Dice loss. Each one of them contributes individually to improve performance further details of loss functions are mentioned below, (1) BCE Loss calculates probabilities and compare