*Result*: Improving Ovarian Cancer Subtyping with Computer Vision Models on Tiled Histopathological Images.
Title:
Improving Ovarian Cancer Subtyping with Computer Vision Models on Tiled Histopathological Images.
Authors:
Ramroach S; Department of Computing and Information Technology, University of the West Indies, St. Augustine, Trinidad and Tobago. sramroach@gmail.com., Hosein R; Department of Computing and Information Technology, University of the West Indies, St. Augustine, Trinidad and Tobago.
Source:
Journal of imaging informatics in medicine [J Imaging Inform Med] 2026 Feb; Vol. 39 (1), pp. 620-626. Date of Electronic Publication: 2025 May 20.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Cham, Switzerland] : Springer Nature, [2024]-
MeSH Terms:
References:
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Bashashati, A., Farahani, H., Consortium, O., Karnezis, A., Akbari, A., Kim, S., Chow, A., Dane, S., Zhang, A., Asadi, M.: UBC Ovarian Cancer Subtype Classification and Outlier Detection (UBC-OCEAN). https://kaggle.com/competitions/UBC-OCEAN . Kaggle (2023).
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Farahani, H., Boschman, J., Farnell, D., Darbandsari, A., Zhang, A., Ahmadvand, P., Jones, S.J., Huntsman, D., Köbel, M., Gilks, C.B., et al: Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images. Modern Pathology 35(12), 1983–1990 (2022). (PMID: 10.1038/s41379-022-01146-z36065012)
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114 (2019). PMLR.
Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: Generalizing residual architectures. arXiv preprint arXiv:1603.08029 (2016).
Jiang, P., Ergu, D., Liu, F., Cai, Y., Ma, B.: A review of yolo algorithm developments. Procedia computer science 199, 1066–1073 (2022). (PMID: 10.1016/j.procs.2022.01.135)
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019).
Chen, B., Zhao, L., Yang, R., Xu, T.: New insights about endometriosis-associated ovarian cancer: pathogenesis, risk factors, prediction and diagnosis and treatment. Frontiers in Oncology 14, 1329133 (2024). (PMID: 10.3389/fonc.2024.13291333838481210879431)
Barreñada, L., Ledger, A., Dhiman, P., Collins, G., Wynants, L., Verbakel, J.Y., Timmerman, D., Valentin, L., Van Calster, B.: Adnex risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies. BMJ medicine 3(1) (2024).
Liu, P., Liu, J., Liu, J., Yu, X.: Investigating the mechanisms of drug resistance and prognosis in ovarian cancer using single-cell rna sequencing and bulk rna sequencing. Aging (Albany NY) 16(5), 4736 (2024). (PMID: 3846142410968697)
Veneziani, A.C., Gonzalez-Ochoa, E., Alqaisi, H., Madariaga, A., Bhat, G., Rouzbahman, M., Sneha, S., Oza, A.M.: Heterogeneity and treatment landscape of ovarian carcinoma. Nature Reviews Clinical Oncology 20(12), 820–842 (2023). (PMID: 10.1038/s41571-023-00819-137783747)
Magdy, A., Hussein, H., Abdel-Kader, R.F., Abd El Salam, K.: Performance enhancement of skin cancer classification using computer vision. IEEE Access (2023).
He, X., Liu, X., Zuo, F., Shi, H., Jing, J.: Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. In: Seminars in Cancer Biology, vol. 88, pp. 187–200 (2023). Elsevier.
Li, S., Jiang, H., Wang, Z., Zhang, G., Yao, Y.-d.: An effective computer aided diagnosis model for pancreas cancer on pet/ct images. Computer methods and programs in biomedicine 165, 205–214 (2018).
Ziyambe, B., Yahya, A., Mushiri, T., Tariq, M.U., Abbas, Q., Babar, M., Albathan, M., Asim, M., Hussain, A., Jabbar, S.: A deep learning framework for the prediction and diagnosis of ovarian cancer in pre-and post-menopausal women. Diagnostics 13(10), 1703 (2023). (PMID: 10.3390/diagnostics131017033723818810217055)
Kodipalli, A., Devi, S., Dasar, S., Ismail, T.: Segmentation and classification of ovarian cancer based on conditional adversarial image to image translation approach. Expert Systems, 13193 (2022).
Gadducci, A., Multinu, F., Cosio, S., Carinelli, S., Ghioni, M., Aletti, G.D.: Clear cell carcinoma of the ovary: Epidemiology, pathological and biological features, treatment options and clinical outcomes. Gynecologic oncology 162(3), 741–750 (2021). (PMID: 10.1016/j.ygyno.2021.06.03334247767)
Zhou, L., Yao, L., Dai, L., Zhu, H., Ye, X., Wang, S., Cheng, H., Ma, R., Liu, H., Cui, H., et al: Ovarian endometrioid carcinoma and clear cell carcinoma: A 21-year retrospective study. Journal of ovarian research 14, 1–12 (2021). (PMID: 10.1186/s13048-021-00804-1)
Kandalaft, L.E., Dangaj Laniti, D., Coukos, G.: Immunobiology of high-grade serous ovarian cancer: lessons for clinical translation. Nature Reviews Cancer 22(11), 640–656 (2022). (PMID: 10.1038/s41568-022-00503-z36109621)
Babaier, A., Mal, H., Alselwi, W., Ghatage, P.: Low-grade serous carcinoma of the ovary: the current status. Diagnostics 12(2), 458 (2022). (PMID: 10.3390/diagnostics12020458352045498871133)
Talia, K.L., Parra-Herran, C., McCluggage, W.G.: Ovarian mucinous and seromucinous neoplasms: problematic aspects and modern diagnostic approach. Histopathology 80(2), 255–278 (2022). (PMID: 10.1111/his.1439933963606)
Szyposzynska, A., Bielawska-Pohl, A., Murawski, M., Sozanski, R., Chodaczek, G., Klimczak, A.: Mesenchymal stem cell microvesicles from adipose tissue: unraveling their impact on primary ovarian cancer cells and their therapeutic opportunities. International Journal of Molecular Sciences 24(21), 15862 (2023). (PMID: 10.3390/ijms2421158623795884410647545)
Brown, Y., Hua, S., Tanwar, P.S.: Extracellular matrix in high-grade serous ovarian cancer: Advances in understanding of carcinogenesis and cancer biology. Matrix Biology 118, 16–46 (2023). (PMID: 10.1016/j.matbio.2023.02.00436781087)
Wang, X., Chen, H., Gan, C., Lin, H., Dou, Q., Huang, Q., Cai, M., Heng, P.-A.: Weakly supervised learning for whole slide lung cancer image classification. In: Medical Imaging with Deep Learning (2022).
Gao, Z., Hong, B., Li, Y., Zhang, X., Wu, J., Wang, C., Zhang, X., Gong, T., Zheng, Y., Meng, D., et al: A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images. Medical Image Analysis 83, 102652 (2023).
Gecer, B., Aksoy, S., Mercan, E., Shapiro, L.G., Weaver, D.L., Elmore, J.G.: Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern recognition 84, 345–356 (2018). (PMID: 10.1016/j.patcog.2018.07.022306798796342566)
Kasture, K.: Ovariancancer &subtypes. Mendeley Data 1 (2021).
Bashashati, A., Farahani, H., Consortium, O., Karnezis, A., Akbari, A., Kim, S., Chow, A., Dane, S., Zhang, A., Asadi, M.: UBC Ovarian Cancer Subtype Classification and Outlier Detection (UBC-OCEAN). https://kaggle.com/competitions/UBC-OCEAN . Kaggle (2023).
Asadi-Aghbolaghi, M., Farahani, H., Zhang, A., Akbari, A., Kim, S., Chow, A., Dane, S., Consortium, O.C., Consortium, O., G Huntsman, D., et al.: Machine learning-driven histotype diagnosis of ovarian carcinoma: Insights from the ocean ai challenge. medRxiv, 2024–04 (2024).
Farahani, H., Boschman, J., Farnell, D., Darbandsari, A., Zhang, A., Ahmadvand, P., Jones, S.J., Huntsman, D., Köbel, M., Gilks, C.B., et al: Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images. Modern Pathology 35(12), 1983–1990 (2022). (PMID: 10.1038/s41379-022-01146-z36065012)
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114 (2019). PMLR.
Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: Generalizing residual architectures. arXiv preprint arXiv:1603.08029 (2016).
Jiang, P., Ergu, D., Liu, F., Cai, Y., Ma, B.: A review of yolo algorithm developments. Procedia computer science 199, 1066–1073 (2022). (PMID: 10.1016/j.procs.2022.01.135)
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019).
Contributed Indexing:
Keywords: Cancer classification; Computer vision; Ovarian cancer
Entry Date(s):
Date Created: 20250520 Date Completed: 20260219 Latest Revision: 20260222
Update Code:
20260222
PubMed Central ID:
PMC12920868
DOI:
10.1007/s10278-025-01546-y
PMID:
40392413
Database:
MEDLINE
*Further Information*
*Ovarian cancer remains one of the most challenging cancers to diagnose due to its non-specific symptoms, lack of reliable screening tests, and the complexity of detecting abnormalities. Accurate subtype classification is crucial for personalised treatment and improved patient outcomes. In this study, we developed a machine learning pipeline fine-tuning pre-trained computer vision models to classify ovarian cancer subtypes from whole slide images (WSI). Using targeted tissue masks for necrosis, stroma, and tumour regions as a proof of concept, we demonstrated the efficacy of tiling masked regions to transform a complex detection-then-classification problem into a simpler classification task. Our method achieved high accuracy in tile-level classification, with a subsequent extension to subtype classification via majority voting on tiled images. Precision exceeds 90% across subtypes, which highlights the potential of scalable, automated systems to assist in ovarian cancer diagnostics. These findings contribute to the broader field of computational pathology, paving the way for enhanced diagnostic consistency and accessibility in clinical settings.
(© 2025. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)*
*Declarations. Conflict of Interest: The authors declare no competing interests.*