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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 usefulin many downstream clinical applications, how to extract them from theradiology reports is less studied. This paper outlines a novel deep learningmethod to automatically extract attributes of lesions of interest from theclinical text. Different from classical CNN models, we integrated themulti-head self-attention mechanism to handle the long-distance information inthe sentence, and to jointly correlate different portions of sentencerepresentation subspaces in parallel. Evaluation on an in-house corpusdemonstrates that our method can achieve high performance with 0.848 inprecision, 0.788 in recall, and 0.815 in F-score. The new method andconstructed corpus will enable us to build automatic systems with ahigher-level understanding of the radiological world.

A self-attention based deep learning method for lesion attribute detection from CT reports