Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study.

ABSTRACT: Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual presentation of rare conditions by studying a few representative examples, teaching a machine to learn from such a "long-tailed" distribution is much more difficult, as standard methods would be easily biased toward the most frequent classes. In this paper, we present a comprehensive benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays. We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common "head" classes, but also the rare yet critical "tail" classes. To accomplish this, we introduce a cha

SUBMITTER: Holste G 

PROVIDER: S-EPMC9618235 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature