Proceedings

Curriculum Hierarchical Knowledge Distillation for Bias-Free Survival Prediction

Chaozhuo Li, Zhihao Tang, Mingji Zhang, Zhiquan Liu, Litian Zhang, Xi Zhang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1332-1340.

https://doi.org/10.24963/ijcai.2025/149

Keywords

  • Computer Vision: CV: Biomedical image analysis

Abstract

Survival prediction is a pivotal task for estimating mortality risk within a given timeframe based on whole slide images (WSIs). Conventional models typically assume that WSIs across patients are independent and identically distributed, an assumption that may not hold due to inherent variability in WSI preparation and the uncertain condition of infected tissues. These uncontrollable external factors introduce significant variability in the numbers and resolutions of WSIs across patients, leading to bias and compromised performance, particularly for tail patients with limited data. In this paper, we propose a novel approach, PathoKD, based on knowledge distillation. Recognizing the hierarchical nature of disease progression and the data scarcity issues associated with vanilla knowledge distillation methods, PathoKD integrates a novel curriculum learning framework with hierarchical knowledge distillation. This integration effectively mitigates the performance gap between head and tail patients, thereby enhancing prediction accuracy across patient groups. Our proposal is extensively evaluated over popular datasets and experimental results demonstrate its superiority.

Chaozhuo Li, Zhihao Tang, Mingji Zhang, Zhiquan Liu, Litian Zhang, and Xi Zhang. 2025. Curriculum Hierarchical Knowledge Distillation for Bias-Free Survival Prediction. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 1332–1340. https://doi.org/10.24963/ijcai.2025/149
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