Medical images of coronary artery plaque are always accompanied by the situation of extreme class imbalance. The traditional two-step methods locate the region of interest (ROI) in the sample firstly, and then segment the sample within the ROI. On the other hand, the traditional resampling methods use resampling strategies to increase the number of minority class samples to mitigate the effects of class imbalance. These two types of methods either make the network structure more complex or decrease training efficiency and performance of the model due to the increase of samples. This paper proposes a method including a novel focal weighted accuracy loss function and improved metrics evaluation algorithms to address the issues in the segmentation of coronary artery calcification plaque mentioned above. Experimental results on the selected dataset show the proposed method increased the training speed and improved the segmentation performance of the model without performing resampling on the dataset. Specifically, the F1-score was 0.873 5, the precision was 0.929 6, and the recall was 0.823 8. The F1-score was largely improved compared with the method using focal loss function. Furthermore, compared with methods with multiple models and methods via resampling the minority class samples, research results demonstrate that the proposed method improved the accuracy and efficiency in coronary artery plaque segmentation while has a shorter training time, which lays the foundation for improving the efficiency and scientific nature of diagnosing related diseases in the future.
Medical image segmentation based on deep learning has become a powerful tool in the field of medical image processing. Due to the special nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positive, false negative, etc. In view of these problems, researchers mostly improve the network structure, but rarely improve from the unstructured aspect. The loss function is an important part of the segmentation method based on deep learning. The improvement of the loss function can improve the segmentation effect of the network from the root, and the loss function is independent of the network structure, which can be used in various network models and segmentation tasks in plug and play. Starting from the difficulties in medical image segmentation, this paper first introduces the loss function and improvement strategies to solve the problems of sample imbalance, edge blur, false positive and false negative. Then the difficulties encountered in the improvement of the current loss function are analyzed. Finally, the future research directions are prospected. This paper provides a reference for the reasonable selection, improvement or innovation of loss function, and guides the direction for the follow-up research of loss function.
The evaluation of disability grades in traffic accidents is a professional forensic clinical appraisal matter, and its results directly affect the fairness of judicial compensation. In the construction of automated disability grade evaluation models, the imbalanced distribution of disability cases leads to low recognition accuracy for minority categories, becoming a key bottleneck restricting the technology's implementation. In response, this paper proposes an imbalanced data classification method based on a hybrid parameter scaling weight optimization mechanism. First, a loss weight calculation model is constructed based on category proportion, category sparsity, and category diversity. Second, the loss weight calculation model is designed by integrating the focal loss function's ability to focus on hard samples with the cross-entropy loss function’s global gradient stability advantage. Then, at the early stages of training, the model proposed in this paper aligns sensitivity to imbalanced categories and constructs a low-computational-demand hybrid parameter scaling weight optimization mechanism. Experimental results show that, compared with the best-performing baseline methods, the proposed method significantly improves both accuracy and macro-F1 score on the traffic accident disability grade dataset. It can effectively enhance the classification performance of minority grade categories in imbalanced data and help improve the accuracy of automated appraisal in judicial identification of traffic accident disability grades.