Summary of Research on Cervical Cancer P16/ki-67 Immunohistochemical Dual-staining Image Recognition Algorithm Based on Yolo, by Xiao-jun Wu et al.
Research on Cervical Cancer p16/Ki-67 Immunohistochemical Dual-Staining Image Recognition Algorithm Based on YOLO
by Xiao-Jun Wu, Cai-Jun Zhao, Chun Meng, Hang Wang
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed DSIR-YOLO model is a novel approach for cervical cancer dual-stained image recognition, which fuses the Swin-Transformer module, GAM attention mechanism, multi-scale feature fusion, and EIoU loss function to improve detection performance. The model achieves 92.6% mAP@0.5 and 70.5% mAP@0.5:0.95 in five-fold cross-validation, outperforming YOLOv5s with increased accuracy, recall, and mAP. The influence of dataset quality on detection results is also studied, showing improved performance by controlling sealing property, scale difference, unlabelled cells, and diagonal annotation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new approach for cervical cancer screening using the p16/Ki-67 dual staining method. However, there are issues with mis-detection when applying YOLOv5s directly to cell images. To solve this problem, they develop a novel DSIR-YOLO model that combines several techniques to improve detection performance. |
Keywords
» Artificial intelligence » Attention » Loss function » Recall » Transformer » Yolo
