Extracting and Learning Fine-Grained Labels from Chest Radiographs
This work provides a method for more detailed recognition of findings in chest radiographs, which could benefit radiologists in emergency rooms and intensive care units by offering more specific diagnostic information.
This paper addresses the problem of extracting and learning fine-grained labels from chest radiographs. They developed a new method for extracting 457 fine-grained labels from radiology reports and trained a deep learning model for fine-grained classification, achieving highly accurate label extraction and reliable learning of these labels.
Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today. Recently, a number of researchers have begun working on large chest X-ray datasets to develop deep learning models for recognition of a handful of coarse finding classes such as opacities, masses and nodules. In this paper, we focus on extracting and learning fine-grained labels for chest X-ray images. Specifically we develop a new method of extracting fine-grained labels from radiology reports by combining vocabulary-driven concept extraction with phrasal grouping in dependency parse trees for association of modifiers with findings. A total of 457 fine-grained labels depicting the largest spectrum of findings to date were selected and sufficiently large datasets acquired to train a new deep learning model designed for fine-grained classification. We show results that indicate a highly accurate label extraction process and a reliable learning of fine-grained labels. The resulting network, to our knowledge, is the first to recognize fine-grained descriptions of findings in images covering over nine modifiers including laterality, location, severity, size and appearance.