Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Understand

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images.

• Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems.
• However, they are basically unsorted and lack semantic annotations like type and location.
• In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion.