通过分子赋能学习实现非专业标注者参与的病理图像分割平民化
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
AI总结：
本研究提出分子赋能学习方案，整合跨模态配准、分子引导标注等技术，结合深度校正学习，利用非专业标注者的部分标签实现更优的病理图像多细胞分割，推动病理AI开发平民化与规模化。
摘要：
高分辨率十亿像素级全切片图像（WSI）中的多类别细胞分割对各类临床应用至关重要。训练这类AI模型通常需要经验丰富的领域专家（如病理学家）进行耗费人力的逐像素手动标注。此外，在通过肉眼区分细粒度细胞类型（如足细胞和系膜细胞）时，这类标注容易出错。本研究评估了仅使用非专业标注者（无医学领域知识的标注者）实现病理AI部署平民化的可行性。本文贡献有三：（1）我们提出了一种分子赋能学习方案，利用非专业标注者提供的部分标签进行多类别细胞分割；（2）所提方法整合了十亿像素级分子-形态跨模态配准、分子引导标注和分子导向分割模型，使得3名非专业标注者的标注效果优于2名经验丰富的病理学家；（3）提出了一种深度校正学习（利用不完美标签学习）方法，利用部分标注的含噪数据进一步提升分割性能。实验结果显示，我们的学习方法使用非专业标注者的分子引导标注达到了F1=0.8496，优于经验丰富的病理学家提供的传统基于形态学的标注（F1=0.7015）。我们的方法将病理分割深度模型的开发平民化至非专业标注者层面，从而使学习过程可像非医学计算机视觉任务一样规模化扩展。官方实现和细胞标注已公开于https://github.com/hrlblab/MolecularEL。
英文摘要：
Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell types (e.g., podocyte and mesangial cells) via the naked human eye. In this study, we assess the feasibility of democratizing pathological AI deployment by only using lay annotators (annotators without medical domain knowledge). The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data. From the experimental results, our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators, which is better than conventional morphology-based annotations (F1 = 0.7015) from experienced pathologists. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level, which consequently scales up the learning process similar to a non-medical computer vision task. The official implementation and cell annotations are publicly available at https://github.com/hrlblab/MolecularEL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
