  In radiology, radiologists not only detect lesions from the medical image,
but also describe them with various attributes such as their type, location,
size, shape, and intensity. While these lesion attributes are rich and useful
in many downstream clinical applications, how to extract them from the
radiology reports is less studied. This paper outlines a novel deep learning
method to automatically extract attributes of lesions of interest from the
clinical text. Different from classical CNN models, we integrated the
multi-head self-attention mechanism to handle the long-distance information in
the sentence, and to jointly correlate different portions of sentence
representation subspaces in parallel. Evaluation on an in-house corpus
demonstrates that our method can achieve high performance with 0.848 in
precision, 0.788 in recall, and 0.815 in F-score. The new method and
constructed corpus will enable us to build automatic systems with a
higher-level understanding of the radiological world.
