DRFL: Dynamic-Recall Focal Loss for Image Classification and Segmentation
The accuracy of neural networks heavily depends on numerous precisely annotated samples. But in actual datasets, the number of samples for each category varies greatly. In the dataset, some classes may have a large sample size, called majority classes, while others may have a small sample size, call...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2411845 |
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| _version_ | 1846119956577517568 |
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| author | Xiaohong Liu Lin Wang Lijing Ma Chaoli Wang |
| author_facet | Xiaohong Liu Lin Wang Lijing Ma Chaoli Wang |
| author_sort | Xiaohong Liu |
| collection | DOAJ |
| description | The accuracy of neural networks heavily depends on numerous precisely annotated samples. But in actual datasets, the number of samples for each category varies greatly. In the dataset, some classes may have a large sample size, called majority classes, while others may have a small sample size, called minority classes. Training model with imbalanced data is often conducive to the majority classes, while unfair to minority classes. As a result, the outputs return good performance on majority classes and bad performance on minority classes. This article proposes a new loss function called dynamic-recall focal loss (DRFL), which can solve the problem of imbalanced data categories in image classification and medical segmentation tasks. The DRFL assigns different weight coefficient to the classes according to their dynamic recall based on focal loss. Experimental results have shown that the newly proposed loss function DRFL can effectively improve the classification and segmentation accuracy of two imbalanced datasets. |
| format | Article |
| id | doaj-art-db73953c903a40c09725b264ac6b55ef |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-db73953c903a40c09725b264ac6b55ef2024-12-16T16:13:01ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2411845DRFL: Dynamic-Recall Focal Loss for Image Classification and SegmentationXiaohong Liu0Lin Wang1Lijing Ma2Chaoli Wang3Department of Radiology, Shanghai Eighth People’s Hospital, Shanghai, ChinaDepartment of Computer Science, East China Normal, Shanghai, ChinaDepartment of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaDepartment of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaThe accuracy of neural networks heavily depends on numerous precisely annotated samples. But in actual datasets, the number of samples for each category varies greatly. In the dataset, some classes may have a large sample size, called majority classes, while others may have a small sample size, called minority classes. Training model with imbalanced data is often conducive to the majority classes, while unfair to minority classes. As a result, the outputs return good performance on majority classes and bad performance on minority classes. This article proposes a new loss function called dynamic-recall focal loss (DRFL), which can solve the problem of imbalanced data categories in image classification and medical segmentation tasks. The DRFL assigns different weight coefficient to the classes according to their dynamic recall based on focal loss. Experimental results have shown that the newly proposed loss function DRFL can effectively improve the classification and segmentation accuracy of two imbalanced datasets.https://www.tandfonline.com/doi/10.1080/08839514.2024.2411845 |
| spellingShingle | Xiaohong Liu Lin Wang Lijing Ma Chaoli Wang DRFL: Dynamic-Recall Focal Loss for Image Classification and Segmentation Applied Artificial Intelligence |
| title | DRFL: Dynamic-Recall Focal Loss for Image Classification and Segmentation |
| title_full | DRFL: Dynamic-Recall Focal Loss for Image Classification and Segmentation |
| title_fullStr | DRFL: Dynamic-Recall Focal Loss for Image Classification and Segmentation |
| title_full_unstemmed | DRFL: Dynamic-Recall Focal Loss for Image Classification and Segmentation |
| title_short | DRFL: Dynamic-Recall Focal Loss for Image Classification and Segmentation |
| title_sort | drfl dynamic recall focal loss for image classification and segmentation |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2411845 |
| work_keys_str_mv | AT xiaohongliu drfldynamicrecallfocallossforimageclassificationandsegmentation AT linwang drfldynamicrecallfocallossforimageclassificationandsegmentation AT lijingma drfldynamicrecallfocallossforimageclassificationandsegmentation AT chaoliwang drfldynamicrecallfocallossforimageclassificationandsegmentation |