The Detection of Thoracic Abnormalities ChestX-Det10 Challenge Results
Jie Lian, Jingyu Liu, Yizhou Yu, Mengyuan Ding, Yaoci Lu, Yi Lu, Jie, Cai, Deshou Lin, Miao Zhang, Zhe Wang, Kai He, Yijie Yu
TL;DR
This paper reports on the results of a challenge for detecting thoracic abnormalities using the ChestX-Det10 dataset, highlighting the performance of six top teams in a novel dataset with detailed annotations.
Contribution
It introduces the ChestX-Det10 dataset with instance-level annotations for thoracic abnormalities and presents challenge results from six top-performing teams.
Findings
Six teams reached the second round of the challenge.
ChestX-Det10 dataset contains 3,543 images with detailed annotations.
The dataset is split into 3001 training and 542 testing images.
Abstract
The detection of thoracic abnormalities challenge is organized by the Deepwise AI Lab. The challenge is divided into two rounds. In this paper, we present the results of 6 teams which reach the second round. The challenge adopts the ChestX-Det10 dateset proposed by the Deepwise AI Lab. ChestX-Det10 is the first chest X-Ray dataset with instance-level annotations, including 10 categories of disease/abnormality of 3,543 images. The annotations are located at https://github.com/Deepwise-AILab/ChestX-Det10-Dataset. In the challenge, we randomly split all data into 3001 images for training and 542 images for testing.
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Taxonomy
TopicsCOVID-19 diagnosis using AI · Lung Cancer Diagnosis and Treatment · Radiomics and Machine Learning in Medical Imaging