Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays.

ABSTRACT: Creating a large-scale dataset of abnormality annotation on medical images is a labor-intensive and costly task. Leveraging weak supervision from readily available data such as radiology reports can compensate lack of large-scale data for anomaly detection methods. However, most of the current methods only use image-level pathological observations, failing to utilize the relevant anatomy mentions in reports. Furthermore, Natural Language Processing (NLP)-mined weak labels are noisy due to label sparsity and linguistic ambiguity. We propose an Anatomy-Guided chest X-ray Network (AGXNet) to address these issues of weak annotation. Our framework consists of a cascade of two networks, one responsible for identifying anatomical abnormalities and the second responsible for patholog

SUBMITTER: Yu K 

PROVIDER: S-EPMC11215940 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature