AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025450095
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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*
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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/ )
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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.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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