用多个病理大模型融合提升非典型有丝分裂识别准确率
Ensemble of Pathology Foundation Models for MIDOG 2025 Track 2: Atypical Mitosis Classification
- 用低秩适配微调病理大模型，结合ConvNeXt V2增强特征提取
- 通过鱼眼变换和傅里叶域自适应优化图像，提升有丝分裂检测
- 集成多个模型实现高平衡准确率，适合医学影像诊断研究者
有丝分裂分为典型与非典型两类，非典型数量与肿瘤侵袭性高度相关，准确区分对预后判断和医疗资源分配至关重要，但即便对专家也极具挑战。本文利用在大规模病理数据上预训练的病理基础模型（PFMs），采用低秩适配进行参数高效微调，并引入ConvNeXt V2这一先进卷积架构以补充模型能力。训练中应用鱼眼变换突出有丝分裂区域，结合使用ImageNet目标图像的傅里叶域自适应。最终通过集成多个PFM，融合互补形态学信息，在初步评估阶段数据集上取得具有竞争力的平衡准确率。
原文摘要 · Abstract (English)
Mitotic figures are classified into typical and atypical variants, with atypical counts correlating strongly with tumor aggressiveness. Accurate differentiation is therefore essential for patient prognostication and resource allocation, yet remains challenging even for expert pathologists. Here, we leveraged Pathology Foundation Models (PFMs) pre-trained on large histopathology datasets and applied parameter-efficient fine-tuning via low-rank adaptation. In addition, we incorporated ConvNeXt V2, a state-of-the-art convolutional neural network architecture, to complement PFMs. During training, we employed a fisheye transform to emphasize mitoses and Fourier Domain Adaptation using ImageNet target images. Finally, we ensembled multiple PFMs to integrate complementary morphological insights, achieving competitive balanced accuracy on the Preliminary Evaluation Phase dataset.
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