PI-YOLO: dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological images Article Swipe
Cong Li
,
Shuanlong Che
,
Haotian Gong
,
Youde Ding
,
Yizhou Luo
,
Jianing Xi
,
Qi Ling
,
Guiying Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2024.1347123
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2024.1347123
Vessel density within tumor tissues strongly correlates with tumor proliferation and serves as a critical marker for tumor grading. Recognition of vessel density by pathologists is subject to a strong inter-rater bias, thus limiting its prognostic value. There are many challenges in the task of object detection in pathological images, including complex image backgrounds, dense distribution of small targets, and insignificant differences between the features of the target to be detected and the image background. To address these problems and thus help physicians quantify blood vessels in pathology images, we propose Pathological Images-YOLO (PI-YOLO), an enhanced detection network based on YOLOv7. PI-YOLO incorporates the BiFormer attention mechanism, enhancing global feature extraction and accelerating processing for regions with subtle differences. Additionally, it introduces the CARAFE upsampling module, which optimizes feature utilization and information retention for small targets. Furthermore, the GSConv module improves the ELAN module, reducing model parameters and enhancing inference speed while preserving detection accuracy. Experimental results show that our proposed PI-YOLO network has higher detection accuracy compared to Faster-RCNN, SSD, RetinaNet, YOLOv5 network, and the latest YOLOv7 network, with a mAP value of 87.48%, which is 2.83% higher than the original model. We also validated the performance of this network on the ICPR 2012 mitotic dataset with an F1 value of 0.8678, outperforming other methods, demonstrating the advantages of our network in the task of target detection in complex pathology images.
Related Topics
Concepts
Computer science
Artificial intelligence
Object detection
Convolutional neural network
Pattern recognition (psychology)
Upsampling
Computer vision
Feature (linguistics)
Feature extraction
Deep learning
Inference
Grading (engineering)
Image (mathematics)
Philosophy
Linguistics
Civil engineering
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fonc.2024.1347123
- https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1347123/pdf
- OA Status
- gold
- Cited By
- 2
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401101622
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401101622Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fonc.2024.1347123Digital Object Identifier
- Title
-
PI-YOLO: dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-29Full publication date if available
- Authors
-
Cong Li, Shuanlong Che, Haotian Gong, Youde Ding, Yizhou Luo, Jianing Xi, Qi Ling, Guiying ZhangList of authors in order
- Landing page
-
https://doi.org/10.3389/fonc.2024.1347123Publisher landing page
- PDF URL
-
https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1347123/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1347123/pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Object detection, Convolutional neural network, Pattern recognition (psychology), Upsampling, Computer vision, Feature (linguistics), Feature extraction, Deep learning, Inference, Grading (engineering), Image (mathematics), Philosophy, Linguistics, Civil engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
-
54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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