A deep learning based assisted analysis approach for Sjogren's syndrome pathology images.
Attention mechanism
Deep learning
IoU loss function
Sjogren’s Syndrome
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
21 10 2024
21 10 2024
Historique:
received:
03
06
2024
accepted:
09
10
2024
medline:
22
10
2024
pubmed:
22
10
2024
entrez:
21
10
2024
Statut:
epublish
Résumé
Diagnosing Sjogren's syndrome requires considerable time and effort from physicians, primarily because it necessitates rigorously establishing the presence lymphatic infiltration in the pathological tissue of the labial gland. The aim of this study is to use deep learning techniques to overcome these limitations and improve diagnostic accuracy and efficiency in pathology. We develop an auxiliary diagnostic system for Sjogren's syndrome. The system incorporates the state-of-the-art object detection neural network, YOLOv8, and enables the precise identification and flagging of suspicious lesions. We design the multi-dimensional attention module and S-MPDIoU loss function to improve the detection performance of YOLOv8. By extracting features from multiple dimensions of the feature map, the utilization of the multi-dimensional attention mechanism enhances the feature interaction across disparate positions, enabling the network to proficiently learn and retain salient cell features. S-MPDIoU introduces an angle penalty term that efficiently minimizes the diagonal distance between predicted and ground truth boxes. Additionally, it incorporates a flexible scale factor tailored to different size feature maps, which balances the issue of sudden gradient decrease during high overlap, thereby accelerating the overall convergence rate. To verify the effectiveness of our methods, we create a dataset of lymphocytes using labial gland biopsy pathology images collected from YanTaiShan hospital and trained the model with this dataset. The proposed model is assessed using standard metrics like precision, recall, mAP. The improved model achieves an increase in recall by 9.1%, mAP.5 by 3.2%, and mAP.95 by 2%. The study demonstrated deep learning's potential to analysis pathology images, offering a reference framework for the application of deep learning technology in the medical domain.
Identifiants
pubmed: 39433593
doi: 10.1038/s41598-024-75925-w
pii: 10.1038/s41598-024-75925-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
24693Subventions
Organisme : Natural Science Foundation of Shandong Province
ID : ZR2020QH244
Organisme : China Primary Health Care Foundation
ID : cphcf-2023-224
Informations de copyright
© 2024. The Author(s).
Références
Fox, R. I., Howell, F. V., Bone, R. C. & Michelson, P. E. Primary sjogren syndrome: Clinical and immunopathologic features. in Seminars in Arthritis and Rheumatism, vol. 14, 77–105 (Elsevier, 1984).
Naga Srinivasu, P., Ijaz, M. F. & Woźniak, M. Xai-driven model for crop recommender system for use in precision agriculture. Comput. Intell. 40, e12629 (2024).
doi: 10.1111/coin.12629
Ibrahim, A. M. & Mohammed, M. A. A comprehensive review on advancements in artificial intelligence approaches and future perspectives for early diagnosis of parkinson’s disease. Int. J. Math. Stat. Comput. Sci. 2, 173–182 (2024).
doi: 10.59543/ijmscs.v2i.8915
Abdulmajeed, N. Q., Al-Khateeb, B. & Mohammed, M. A. Voice pathology identification system using a deep learning approach based on unique feature selection sets. Expert Syst. e13327 (2023).
Shen, W., Zhou, M., Yang, F., Yang, C. & Tian, J. Multi-scale convolutional neural networks for lung nodule classification. in Information Processing in Medical Imaging: 24th International Conference, IPMI 2015, Sabhal Mor Ostaig, Isle of Skye, UK, June 28-July 3, 2015, Proceedings 24, 588–599 (Springer, 2015).
Ho, D. J. et al. Deep multi-magnification networks for multi-class breast cancer image segmentation. Comput. Med. Imaging Graph. 88, 101866 (2021).
doi: 10.1016/j.compmedimag.2021.101866
pubmed: 33485058
pmcid: 7975990
Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, 779–788 (2016).
Redmon, J. & Farhadi, A. Yolo9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7263–7271 (2017).
Redmon, J. Yolov3: An incremental improvement. arXiv preprint. arXiv:1804.02767 (2018).
Bochkovskiy, A. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint. arXiv:2004.10934 (2020).
Wang, C.-Y., Bochkovskiy, A. & Liao, H.-Y. M. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 7464–7475 (2023).
Wang, C.-Y., Yeh, I.-H. & Liao, H.-Y. M. Yolov9: Learning what you want to learn using programmable gradient information. arXiv preprint. arXiv:2402.13616 (2024).
Wang, A. et al. Yolov10: Real-time end-to-end object detection. arXiv preprint. arXiv:2405.14458 (2024).
Rezatofighi, H. et al. Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 658–666 (2019).
Zheng, Z. et al. Distance-iou loss: Faster and better learning for bounding box regression. Proc. AAAI Conf. Artif. Intell. 34, 12993–13000 (2020).
Zheng, Z. et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans. Cybernetics 52, 8574–8586 (2021).
doi: 10.1109/TCYB.2021.3095305
Zhang, Y.-F. et al. Focal and efficient iou loss for accurate bounding box regression. Neurocomputing 506, 146–157 (2022).
doi: 10.1016/j.neucom.2022.07.042
Gevorgyan, Z. Siou loss: More powerful learning for bounding box regression. arXiv preprint. arXiv:2205.12740 (2022).
Siliang, M. & Yong, X. Mpdiou: a loss for efficient and accurate bounding box regression. arXiv preprint. arXiv:2307.07662 (2023).
Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7132–7141 (2018).
Woo, S., Park, J., Lee, J.-Y. & Kweon, I. S. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV), 3–19 (2018).
Liu, Y., Shao, Z. & Hoffmann, N. Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv preprint. arXiv:2112.05561 (2021).
Zhang, Q.-L. & Yang, Y.-B. Sa-net: Shuffle attention for deep convolutional neural networks. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2235–2239 (IEEE, 2021).
Hou, Q., Zhou, D. & Feng, J. Coordinate attention for efficient mobile network design. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 13713–13722 (2021).
Wang, Q. et al. Eca-net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 11534–11542 (2020).
Ouyang, D. et al. Efficient multi-scale attention module with cross-spatial learning. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5 (IEEE, 2023).
Zhao, Y. et al. Detrs beat yolos on real-time object detection. arXiv preprint. arXiv:2304.08069 (2023).
Wang, C. et al. Gold-yolo: Efficient object detector via gather-and-distribute mechanism. Advances in Neural Information Processing Systems 36 (2024).
Xu, S. et al. Pp-yoloe: An evolved version of yolo. arXiv preprint. arXiv:2203.16250 (2022).