AMLPF-CLIP: Adaptive prompting and distilled learning for imbalanced histopathological image classification

Yao, Xizhang, Yue, Guanghui, Deng, Jeremiah D., Lin, Hanhe and Zhou, Wei 2025. AMLPF-CLIP: Adaptive prompting and distilled learning for imbalanced histopathological image classification. IEEE Journal of Biomedical and Health Informatics 10.1109/jbhi.2025.3619343

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Abstract

Histopathological image classification (HIC) plays a pivotal role in computer-aided diagnosis, enabling lesion characterization (e.g., tumor grading) and survival outcome prediction. Despite recent advances in HIC, existing methods still face challenges in integrating domain-specific knowledge, addressing class imbalance, and ensuring computational efficiency. To address these challenges, we propose AMLPF-CLIP, an enhanced CLIP-based framework for HIC featuring three key innovations. First, we introduce an Adaptive Multi-Level Prompt Fusion (AMLPF) strategy that leverages three levels of textual prompts: class labels, basic descriptions, and GPT-4o-generated detailed pathological features for enhanced semantic representation and cross-modal alignment. Second, we design a class-balanced resampling method that dynamically adjusts sampling weights based on both data imbalance and classification performance, targeting underrepresented, low-confidence classes. Third, we develop a Knowledge Distillation (KD) technique that leverages output-level alignment via L2 loss, transferring knowledge from a large Vision Transformer (ViT-L/16) to a lightweight ResNet-50-based CLIP model. Extensive experiments on three public datasets demonstrate that AMLPF-CLIP consistently outperforms eleven state-of-the-art methods, achieving accuracy improvements of 1.19% on Chaoyang, 2.64% on BreaKHis, and 0.90% on LungHist700. AMLFP-CLIP also demonstrates improved robustness and efficiency, highlighting its practical applicability.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2025-01-01
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2168-2194
Last Modified:	28 Oct 2025 12:00
URI:	https://orca.cardiff.ac.uk/id/eprint/181950

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