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<p>Ovarian cancer encompasses multiple molecular subtypes, each necessitating distinct diagnostic approaches and tailored therapeutic strategies. Achieving accurate and adaptive classification of these subtypes remains a significant challenge, particularly in clinical environments where data distributions evolve over time. To address this issue, this study proposes a novel continual deep learning framework designed to mitigate catastrophic forgetting, a key limitation of incremental learning models. The proposed framework employs a Vision Transformer–based architecture coupled with a task-specific classification head and rehearsal-based memory buffers, enabling the retention of representative knowledge from previously learned samples while facilitating effective adaptation to newly acquired data. Extensive experimental evaluations conducted on a large-scale ovarian cancer dataset demonstrate the robustness and effectiveness of the proposed approach. The model achieved a validation accuracy of 96.2% and a test accuracy of 95.4% across all evaluated ovarian cancer subtypes. Furthermore, consistent and balanced performance was observed, with F1-scores exceeding 0.92 for all subtypes, and classification accuracy of 99% for the clear cell carcinoma subtype. These findings highlight the potential of the proposed continual learning framework to provide reliable, adaptive, and clinically meaningful ovarian cancer subtype classification in real-world diagnostic settings.</p>

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Rehearsal-based continual learning for robust ovarian cancer subtype classification under catastrophic forgetting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Zahraa Tarek,
  • Esraa Hassan

摘要

Ovarian cancer encompasses multiple molecular subtypes, each necessitating distinct diagnostic approaches and tailored therapeutic strategies. Achieving accurate and adaptive classification of these subtypes remains a significant challenge, particularly in clinical environments where data distributions evolve over time. To address this issue, this study proposes a novel continual deep learning framework designed to mitigate catastrophic forgetting, a key limitation of incremental learning models. The proposed framework employs a Vision Transformer–based architecture coupled with a task-specific classification head and rehearsal-based memory buffers, enabling the retention of representative knowledge from previously learned samples while facilitating effective adaptation to newly acquired data. Extensive experimental evaluations conducted on a large-scale ovarian cancer dataset demonstrate the robustness and effectiveness of the proposed approach. The model achieved a validation accuracy of 96.2% and a test accuracy of 95.4% across all evaluated ovarian cancer subtypes. Furthermore, consistent and balanced performance was observed, with F1-scores exceeding 0.92 for all subtypes, and classification accuracy of 99% for the clear cell carcinoma subtype. These findings highlight the potential of the proposed continual learning framework to provide reliable, adaptive, and clinically meaningful ovarian cancer subtype classification in real-world diagnostic settings.