The Detection of Thoracic Abnormalities ChestX-Det10 Challenge Results
This work addresses the problem of automated medical image analysis for clinicians, but it appears incremental as it focuses on reporting challenge outcomes without introducing new methods.
The paper presents results from the ChestX-Det10 challenge, which tackled the detection of thoracic abnormalities in chest X-rays using a dataset with instance-level annotations, but it does not report specific performance numbers or results from the teams.
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.