通过分子赋能学习,利用非专业注释器实现病理图像分割的民主化。

Ruining Deng, Yanwei Li, Peize Li, Jiacheng Wang, Lucas W Remedios, Saydolimkhon Agzamkhodjaev, Zuhayr Asad, Quan Liu, Can Cui, Yaohong Wang, Yihan Wang, Yucheng Tang, Haichun Yang, Yuankai Huo
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引用次数: 0

摘要

高分辨率千兆像素全切片图像(WSI)中的多类细胞分割对于各种临床应用至关重要。训练这种人工智能模型通常需要经验丰富的领域专家(如病理学家)进行劳动密集型的像素人工标注。此外,在通过肉眼区分细粒度细胞类型(如荚膜细胞和间质细胞)时,这种标注容易出错。在本研究中,我们评估了仅使用非专业注释者(不具备医学领域知识的注释者)来实现病理人工智能部署民主化的可行性。本文的贡献有三:(1)我们提出了一种利用非专业注释者的部分标签进行多类细胞分割的分子赋能学习方案;(2)所提出的方法集成了千兆像素级分子形态学跨模态注册、分子信息注释和面向分子的分割模型,从而使通过 3 名非专业注释者获得的性能明显优于 2 名经验丰富的病理学家;(3)提出了一种深度矫正学习(不完美标签学习)方法,以进一步提高利用部分注释的噪声数据进行分割的性能。从实验结果来看,我们的学习方法利用非专业注释者的分子信息注释达到了 F1 = 0.8496,优于经验丰富的病理学家基于形态学的传统注释(F1 = 0.7015)。我们的方法将病理分割深度模型的开发民主化,使其达到非专业注释者的水平,从而将学习过程扩展到类似于非医学计算机视觉任务。正式实现和细胞注释可在 https://github.com/hrlblab/MolecularEL 上公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning.

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.

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