AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025450095
Submit to AIH
Cite this article
3
Download
41
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ORIGINAL RESEARCH ARTICLE

Seg-lite artificial intelligence: Scalable, explainable deep learning for ovarian cancer histopathology on low-resource systems

Abir Banerjee1 ,  Amarjeet Boruah1 ,  Dibya Jyoti Bora1*
Show Less
1 Department of Information Technology, School of Computing Sciences, The Assam Kaziranga University, Jorhat, Assam, India
AIH, 025450095 https://doi.org/10.36922/AIH025450095
Received: 3 November 2025 | Revised: 3 December 2025 | Accepted: 10 December 2025 | Published online: 31 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
XML
Cite
Abstract

Histopathological examination remains essential for ovarian cancer diagnosis, yet routine assessment is time-consuming and depends on specialist interpretation, which may limit prompt clinical decision-making. This study proposes a compact deep-learning framework designed for automated ovarian cancer tissue segmentation that can function effectively on standard laptops and graphics processing units with limited VRAM. The model, a streamlined U-Net containing 237,457 parameters, was trained on 43,265 image patches at 64 × 64 resolution using a 70/15/15 train/validation/test split. Despite its small footprint, it achieved a Dice score, intersection-over-union, and pixel accuracy of 1.000 on the test set, converging in approximately 20 min over six epochs. Gradient-weighted class activation mapping visualization confirmed that the network consistently focused on morphologically relevant structures. These findings demonstrate that high-precision segmentation can be achieved without computationally expensive architectures, providing a practical and scalable solution for resource-limited clinical environments.

Graphical abstract
Keywords
Ovarian cancer
Deep learning
U-Net
Histopathology
Explainable artificial intelligence
Gradient-weighted class activation mapping
Resource-efficient computing
Medical-image segmentation
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
References
  1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. doi: 10.3322/caac.21660

 

  1. Torre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics, 2018. CA Cancer J Clin. 2018;68(4):284-296. doi: 10.3322/caac.21456

 

  1. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88. doi: 10.1016/j.media.2017.07.005

 

  1. Elmore JG, Longton GM, Carney PA, et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA. 2015;313(11):1122-1132. doi: 10.1001/jama.2015.1405

 

  1. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. doi: 10.1038/nature21056

 

  1. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203-211. doi: 10.1038/s41592-020-01008-z

 

  1. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI). Berlin: Springer; 2015. p. 234-241. doi: 10.1007/978-3-319-24574-4_28

 

  1. Cao H, Wang Y, Chen J, et al. Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision Workshops. Springer; 2023. p. 205-218. doi: 10.1007/978-3-031-25066-8_9

 

  1. Zhang L, Chen J, Shen Y, et al. Deep learning-based ovarian cancer subtypes identification using multi-omics data. BioData Min. 2019;12(1):1-13. doi: 10.1186/s13040-019-0209-8

 

  1. Wang S, Liu Z, Rong Y, et al. Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer. Radiother Oncol. 2020;132:171-177. doi: 10.1016/j.radonc.2018.10.019

 

  1. Liu Y, Zhang Y, Wang L, Chen X, Liu X, Zhang D. Multi-scale attention network for ovarian tumor segmentation in histopathology images. Comput Med Imaging Graph. 2021;93:101975. doi: 10.1016/j.compmedimag.2021.101975

 

  1. Chen Y, Zhang Q, Wu Y, Liu B, Wang M, Lin Y. A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models. Med Image Anal. 2023;89:102919. doi: 10.1016/j.media.2023.102919

 

  1. Kumar A, Singh SK, Saxena S, et al. Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer. Inf Sci. 2024;508:405-421. doi: 10.1016/j.ins.2019.08.072

 

  1. Zhao Y, Li X, Zhang W, Zhao Y, Chen M, Gao M. Weakly supervised learning for ovarian cancer detection from histopathology images. In: IEEE International Symposium on Biomedical Imaging (ISBI). 2023. p. 1-5. doi: 10.1109/ISBI53787.2023.10230457

 

  1. Howard AG, Zhu M, Chen B, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. [arXiv preprint]; 2017.

 

  1. Tan M, Le Q. EfficientNet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning (ICML); 2019. p. 6105-6114. Available from: https://proceedings.mlr.press/v97/tan19a.html

 

  1. Mehta S, Rastegari M. MobileViT: Lightweight, general-purpose, and mobile-friendly vision transformer. In: International Conference on Learning Representations (ICLR); 2022. Available from: https://arxiv.org/abs/2110.02178

 

  1. Vasu PKA, Gabriel J, Zhu J, Tuzel O, Ranjan A. FastViT: A fast hybrid vision transformer using structural reparameterization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision; 2023. p. 5785-5795. doi: 10.1109/ICCV51070.2023.00532

 

  1. Hatamizadeh A, Xu Z, Yang D, Li W, Roth H, Xu D. FastSAM: Fast Segment Anything Model for Medical Image Segmentation. [arXiv Preprint]; 2023. 20. Banbury CR, Reddi VJ, Lam M, et al. Benchmarking TinyML Systems: Challenges and Direction. [arXiv Preprint]; 2021.

 

  1. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations from Deep Networks Via Gradient-Based Localization. In: Proceedings of the IEEE International Conference on Computer Vision; 2017. p. 618-626. doi: 10.1109/ICCV.2017.74

 

  1. Holzinger A, Biemann C, Pattichis CS, Kell DB. What do we Need to Build Explainable AI Systems for the Medical Domain? [arXiv Preprint]; 2017.

 

  1. Schlemper J, Oktay O, Schaap M, et al. Attention gated networks: Learning to leverage salient regions in medical images. Med Image Anal. 2019;53:197-207. doi: 10.1016/j.media.2019.01.012

 

  1. Ghorbani A, Wexler J, Zou JY, Kim B. Towards Automatic Concept-Based Explanations. In: Advances in Neural Information Processing Systems 32 (NeurIPS) Annual Conference on Neural Information Processing Systems; 2019.

 

  1. Wang X, Yang S, Zhang J, et al. TransPath: Transformer-based self-supervised learning for histopathological image classification. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI). Berlin: Springer; 2022. p. 186-195. doi: 10.1007/978-3-031-16434-7_18

 

  1. Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310. doi: 10.1186/s12911-020-01332-6

 

  1. UBC Ovarian Cancer Subtype Classification and Outlier Detection (UBC-OCEAN). Kaggle. Available from: https:// kaggle.com/competitions/ubc-ocean.published/2023

 

  1. Stuart C, Brault S, Rowe G, et al. Nestedness and species replacement along bathymetric gradients in the deep sea reflect productivity: A test with polychaete assemblages in the oligotrophic north-west Gulf of Mexico. J Biogeogr. 2016;44:548-555. doi: 10.1111/jbi.12810

 

  1. Shashikala HK, Suresh MB. Performance analysis of segmentation techniques for knee osteoarthritis detection from X-ray images. In: Integration of Federated Learning and Blockchain for Smart Cities. New Jersey, U.S: John Wiley and Sons; 2025. p659-682.

 

  1. Chung NC, Miasojedow B, Startek M, Gambin A. Jaccard/ Tanimoto similarity test and estimation methods for biological presence-absence data. BMC Bioinformatics. 2019;20(Suppl 15):644. doi: 10.1186/s12859-019-3118-5

 

  1. Journal J, Al-Kiyyam M, Shibli R, et al. Evaluation of genetic diversity in Jordanian Solanum nigrum plants and genetic stability of in vitro grown plant using (AFLP) technique. Jordan J Biol Sci. 2024;17:123-128. doi: 10.54319/jjbs/170111

 

  1. Yoo JJ, Namdar K, Khalvati F. Deep superpixel generation and clustering for weakly supervised segmentation of brain tumors in MR images. BMC Med Imaging. 2024;24(1):335. doi: 10.1186/s12880-024-01523-x

 

  1. Hung KL, Luebeck J, Dehkordi SR, et al. Targeted profiling of human extrachromosomal DNA by CRISPR-CATCH. Nat Genet. 2022;54(11):1746-1754. doi: 10.1038/s41588-022-01190-0

 

  1. Saha AK, Rabbani M, Sum ASI, Mridha MF, Kabir MM. An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images. Sci Rep. 2025;15(1):21860. doi: 10.1038/s41598-025-07903-9

 

  1. Asadi-Aghbolaghi M, Farahani H, Zhang A, et al. Machine learning-driven histotype diagnosis of ovarian carcinoma: Insights from the OCEAN AI challenge. [medRxiv Preprint]; 2024. doi: 10.1101/2024.04.19.24306099

 

  1. Garcia-Atutxa I, Martínez-Más J, Bueno-Crespo A, Villanueva-Flores F. Early-fusion hybrid CNN-transformer models for multiclass ovarian tumor ultrasound classification. Front Artif Intell. 2025;8:1679310. doi: 10.3389/frai.2025.1679310

 

  1. Chong PL, Vaigeshwari V, Mohammed Reyasudin BK, et al. Integrating artificial intelligence in healthcare: Applications, challenges, and future directions. Future Sci OA. 2025;11(1):2527505. doi: 10.1080/20565623.2025.2527505

 

  1. Han F, Huang X, Wang X, et al. Artificial intelligence in orthopedic surgery: Current applications, challenges, and future directions. MedComm (2020). 2025;6(7):e70260. doi: 10.1002/mco2.70260

 

  1. Musthafa MM, Mahesh TR, Kumar VV, Guluwadi S. Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50. BMC Med Imaging. 2024;24(1):107. doi: 10.1186/s12880-024-01292-7

 

  1. El-Khoury R, Zaatari G. The rise of AI-assisted diagnosis: Will pathologists be partners or bystanders? Diagnostics (Basel). 2025;15(18):2308. doi: 10.3390/diagnostics15182308

 

  1. Sajjad U, Rezapour M, Su Z, Tozbikian GH, Gurcan MN, Niazi MKK. NRK-ABMIL: Subtle metastatic deposits detection for predicting lymph node metastasis in breast cancer whole-slide images. Cancers (Basel). 2023;15(13):3428. doi: 10.3390/cancers15133428

 

  1. Neary-Zajiczek L, Beresna L, Razavi B, Pawar V, Shaw M, Stoyanov D. Minimum resolution requirements of digital pathology images for accurate classification. Med Image Anal. 2023;89:102891. doi: 10.1016/j.media.2023.102891

 

  1. Babaeipour R, Fox MS, Parraga G, Ouriadov A. Comparative analysis of foundational, advanced, and traditional deep learning models for hyperpolarized gas MRI lung segmentation: Robust performance in data-constrained scenarios. Bioengineering (Basel). 2025;12(10):1062. doi: 10.3390/bioengineering12101062

 

  1. Komura D, Ochi M, Ishikawa S. Machine learning methods for histopathological image analysis: Updates in 2024. Comput Struct Biotechnol J. 2024;27:383-400. doi: 10.1016/j.csbj.2024.12.033

 

  1. Patel ZM, Holbrook EH, Turner JH, et al. International consensus statement on allergy and rhinology: Olfaction. Int Forum Allergy Rhinol. 2022;12(4):327-680. doi: 10.1002/alr.22929

 

  1. Wang J, Zhang S, Li J, et al. Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers. BMC Gastroenterol. 2025;25(1):494. doi: 10.1186/s12876-025-04045-0
Share
Back to top
Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept