PatchSorter：用于物体标记的高通量深度学习数字病理学工具。

IF 12.4 1区医学 Q1 HEALTH CARE SCIENCES & SERVICES

NPJ Digital Medicine Pub Date : 2024-06-20 DOI:10.1038/s41746-024-01150-4

Cédric Walker, Tasneem Talawalla, Robert Toth, Akhil Ambekar, Kien Rea, Oswin Chamian, Fan Fan, Sabina Berezowska, Sven Rottenberg, Anant Madabhushi, Marie Maillard, Laura Barisoni, Hugo Mark Horlings, Andrew Janowczyk

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引用次数: 0

摘要

要发现数字病理图像中与诊断、预后和治疗反应相关的模式，往往需要对大量组织学对象进行难以处理的标记。在此，我们发布了一款开源标注工具 PatchSorter，它将深度学习与直观的网络界面相结合。通过使用 >100,000 个对象，我们证明了每秒标记的数量比无辅助标记提高了 >7 倍，而对标记准确性的影响却微乎其微，从而实现了大型数据集的高通量标记。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

PatchSorter: a high throughput deep learning digital pathology tool for object labeling

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PatchSorter: a high throughput deep learning digital pathology tool for object labeling

The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

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来源期刊

NPJ Digital Medicine Multiple-

CiteScore

25.10

自引率

3.30%

发文量

170

审稿时长

15 weeks

期刊介绍： npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.