用新型微调方法Rein提升病理图像肿瘤分割精度
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models
- 引入可学习标记实现视觉基础模型的参数化微调
- 在跨器官跨扫描仪数据上，任务1/2分别达0.7719/0.8848
- 适合关注医学图像分割与模型泛化能力的研究者
近年来，数字病理学中的肿瘤分割取得了显著进展。然而，器官差异、组织制备方法及成像过程的差异会导致数字病理图像间的域偏差。为解决此问题，本文采用Rein微调方法，对多种视觉基础模型（VFMs）进行参数化且高效微调，用于MICCAI 2024跨器官跨扫描仪腺癌分割挑战赛（COSAS2024）。Rein的核心在于一组可学习标记，这些标记直接关联实例，从而在每一层提升实例级功能。在COSAS2024数据环境下，大量实验表明，使用Rein微调的模型表现优异。具体而言，我们用Rein微调ConvNeXt和DINOv2，前者在任务1的初赛与决赛阶段得分分别为0.7719和0.7557，后者在任务2的初赛与决赛阶段得分分别为0.8848和0.8192。代码已公开于GitHub。
原文摘要 · Abstract (English)
In recent years, significant progress has been made in tumor segmentation within the field of digital pathology. However, variations in organs, tissue preparation methods, and image acquisition processes can lead to domain discrepancies among digital pathology images. To address this problem, in this paper, we use Rein, a fine-tuning method, to parametrically and efficiently fine-tune various vision foundation models (VFMs) for MICCAI 2024 Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation (COSAS2024). The core of Rein consists of a set of learnable tokens, which are directly linked to instances, improving functionality at the instance level in each layer. In the data environment of the COSAS2024 Challenge, extensive experiments demonstrate that Rein fine-tuned the VFMs to achieve satisfactory results. Specifically, we used Rein to fine-tune ConvNeXt and DINOv2. Our team used the former to achieve scores of 0.7719 and 0.7557 on the preliminary test phase and final test phase in task1, respectively, while the latter achieved scores of 0.8848 and 0.8192 on the preliminary test phase and final test phase in task2. Code is available at GitHub.
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