Abstract
The lesion regions of a medical image account for only a small part of the image, and a critical imbalance exists in the distribution of the positive and negative samples, which affects the segmentation performance of the lesion regions. Dice loss is beneficial for the image segmentation involving an extreme imbalance of the positive and negative samples but it ignores the background regions, which also contain a large amount of information. In this work, we propose an improved dice loss that can mine the information in background areas and modify network architecture to improve performance. The improved dice loss called weighted soft dice loss (WSDice loss). Our loss function gives a small weight to the background area of the label, so the background area will be added to the calculation when calculating dice loss. It can also soft the hard label in the lesion area to increase the robustness of the model to noise label. What's more, we propose to cascade Focal loss and WSDice loss. Focal Loss is a Distribution-based loss function, WSDice Loss is a Region-based loss function, the optimization directions of them are different. The cascaded loss function can make full use of the advantages of both and greatly improve model performance. In addition, we add a simple but effective channel attention module to the decode module of U-net. We experimented on the ChestX-ray8 datasets. Compared with Dice loss, WSDice loss improves the dice coefficient by 1.59%, cascaded loss function can improve dice coefficient by 7.81%. The improved in model architecture can increase the dice coefficient by 1.36%.
Highlights
The occurrence of pneumothorax is a medical condition that refers to the gas entrapment caused by a gas entering the pleural cavity
Since the overlap region between the predicted value Yand the true value Y is repeatedly calculated in the denominator, the coefficient is multiplied by two in the numerator when calculating the dice coefficient, which can lead to the formation of a loss function that can be minimized when calculating the dice loss
In this paper, we improve the dice loss in the form of the weighted soft dice loss to solve the problem of the imbalance between the positive and negative samples in the medical image segmentation and the disadvantage that the dice loss only focuses on positive samples and ignores negative samples
Summary
The occurrence of pneumothorax is a medical condition that refers to the gas entrapment caused by a gas entering the pleural cavity. Shen et al generated the weight based on the number of label categories, number of samples and number of pixels in the same category, which was multiplied by the dice loss [16] This method could effectively solve the problem of the imbalance in multiclass segmentation. In the proposed approach, according to the weight of the label generation, the negative sample regions are included in the calculation of the dice coefficient, which can considerably improve the performance of the network segmentation. This value may dominate the loss of the positive samples and lead the network to deeply mine the negative sample regions; this situation is in contrast to the goal of the lesion region segmentation To solve this problem, the focal loss divides all the samples into easy and hard samples according to the confidence degree of the model. Where p is the model’s estimated probability, α, γ are two hyperparameters, α is used to adjust the distribution of the easy samples, and (1 − p)γ is the dynamic scaling factor, which is used to adjust the distribution of the hard samples
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