Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.

Purpose To develop a machine learning approach for classifying disease progression in chest radiographs using weak labels automatically derived from radiology reports. Materials and Methods In this retrospective study, a twin neural network was developed to classify anatomy-specific disease progression into four categories: improved, unchanged, worsened, and new. A two-step weakly supervised learning approach was employed, pretraining the model on 243 008 frontal chest radiographs from 63 877 patients (mean age, 51.

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

Creating a large-scale dataset of abnormality annotation on medical images is a labor-intensive and costly task. Leveraging 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 in reports.