Abstract
Prostate cancer stands as a pressing contemporary health challenge, urging the development of sophisticated diagnostic support systems. In response, digital pathology has emerged as a crucial field, offering novel methods for diagnosis enhancement. Within this discipline lie the methods of Deep Learning applied in medical imaging. Leveraging these advancements, our work uses a Generative Artificial Intelligence model to augment the performance of content-based image retrieval (CBIR) systems in prostate cancer diagnosis. In particular, this work aims to replace the traditional method of generating augmented views as positive views with synthetic samples created by our generative model. Through extensive experimentation, notable enhancements in key evaluation metrics such as top-k accuracy, majority vote, and precision, with k representing the number of retrievals for computing each metric, are showcased. These findings highlight the substantial refinement of latent vector representations within the feature space. This improvement underscores the potential of our method to revolutionize medical image analysis and diagnosis in the realm of prostate cancer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bray, F., Laversanne, M., S.H., et al.: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Can. J. Clin. 74, 1–35 (2024). https://doi.org/10.3322/caac.21834
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Golfe, A., del Amor, R., Colomer, A., Sales, M.A., Terradez, L., Naranjo, V.: Towards the on-demand whole slide image generation: prostate patch synthesis through a conditional progressive growing GAN. In: 2023 31st European Signal Processing Conference (EUSIPCO), pp. 1070–1074. IEEE (2023)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hegde, N., et al.: Similar image search for histopathology: SMILY. NPJ Digit. Med. 2(1), 56 (2019)
Hua, S., Yan, F., Shen, T., Zhang, X.: Pathoduet: foundation models for pathological slide analysis of h &e and ihc stains. arXiv preprint arXiv:2312.09894 (2023)
Jose, A., Yan, S., Heisterklaus, I.: Binary hashing using siamese neural networks. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2916–2920. IEEE (2017)
Krizhevsky, A., Hinton, G.E.: Using very deep autoencoders for content-based image retrieval. In: ESANN, vol. 1, p. 2. Citeseer (2011)
Lu, M.Y., et al.: Towards a visual-language foundation model for computational pathology. arXiv preprint arXiv:2307.12914 (2023)
Silva-Rodríguez, J., Colomer, A., Sales, M.A., Molina, R., Naranjo, V.: Going deeper through the gleason scoring scale: an automatic end-to-end system for histology prostate grading and cribriform pattern detection. Comput. Methods Programs Biomed. 195, 105637 (2020)
Tabatabaei, Z., Colomer, A., Moll, J.O., Naranjo, V.: Towards more transparent and accurate cancer diagnosis with an unsupervised CAE approach. arXiv preprint arXiv:2305.11728 (2023)
Wang, J., et al.: Learning fine-grained image similarity with deep ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1386–1393 (2014)
Wang, X., Du, Y., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., Han, X.: RetCCL: clustering-guided contrastive learning for whole-slide image retrieval. Med. Image Anal. 83, 102645 (2023)
Acknowledgments
This work has received funding from the Spanish Ministry of Economy and Competitiveness through the project PID2022-140189OB-C21 (ASSIST). The work of Alejandro Golfe has been supported by the Valencian Graduate School and Research Network for Artificial Intelligence (valgrAI). The work of Adrián Colomer has been partially supported by “Ayuda a Primeros Proyectos de Investigación (PAID-06-22)”, Vicerrectorado de Investigación de la Universitat Politècnica de València (UPV).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Golfe, A., Colomer, A., Prades, J., Naranjo, V. (2025). Leveraging Synthetic Samples for Boosting CBIR Performance in Prostate Cancer Diagnosis. In: Juan, A.A., Faulin, J., Lopez-Lopez, D. (eds) Decision Sciences. DSA ISC 2024. Lecture Notes in Computer Science, vol 14779. Springer, Cham. https://doi.org/10.1007/978-3-031-78241-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-78241-1_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-78240-4
Online ISBN: 978-3-031-78241-1
eBook Packages: Computer ScienceComputer Science (R0)