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.

本文介绍了 PatchSorter，一款开源标注工具，它将深度学习与直观的 web 界面相结合，能够大幅提高数字病理学图像的标注效率，可用于高通量标注大型数据集。

PatchSorter: 一种高通量的深度学习数字病理学工具用于目标标记

PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for  Object Labeling

Availability of a few, large-size, annotated datasets, like ImageNet, Pascal
VOC and COCO, has lead deep learning to revolutionize computer vision research
by achieving astonishing results in several vision tasks.We argue that new
tools to facilitate generation of annotated datasets may help spreading
data-driven AI throughout applications and domains. In this work we propose
Shooting Labels, the first 3D labeling tool for dense 3D semantic segmentation
which exploits Virtual Reality to render the labeling task as easy and fun as
playing a video-game. Our tool allows for semantically labeling large scale
environments very expeditiously, whatever the nature of the 3D data at hand
(e.g. point clouds, mesh). Furthermore, Shooting Labels efficiently integrates
multiusers annotations to improve the labeling accuracy automatically and
compute a label uncertainty map. Besides, within our framework the 3D
annotations can be projected into 2D images, thereby speeding up also a
notoriously slow and expensive task such as pixel-wise semantic labeling. We
demonstrate the accuracy and efficiency of our tool in two different scenarios:
an indoor workspace provided by Matterport3D and a large-scale outdoor
environment reconstructed from 1000+ KITTI images.

利用虚拟现实技术开发的 Shooting Labels 标注工具，可以快速标注大规模环境的 3D 语义分割数据，同时可以集成多用户标注并计算标签的不确定性，还能将 3D 标注投影到 2D 图像中，提高像素级语义标注的准确性和效率。