IIHT: Medical Report Generation with Image-to-Indicator Hierarchical
  Transformer
Keqiang Fan Xiaohao Cai Mahesan Niranjan
Automated medical report generation has become increasingly important in
medical analysis. It can produce computer-aided diagnosis descriptions and thus
significantly alleviate the doctors' work. Inspired by the huge success of
neural machine translation and image captioning, various deep learning methods
have been proposed for medical report generation. However, due to the inherent
properties of medical data, including data imbalance and the length and
correlation between report sequences, the generated reports by existing methods
may exhibit linguistic fluency but lack adequate clinical accuracy. In this
work, we propose an image-to-indicator hierarchical transformer (IIHT)
framework for medical report generation. It consists of three modules, i.e., a
classifier module, an indicator expansion module and a generator module. The
classifier module first extracts image features from the input medical images
and produces disease-related indicators with their corresponding states. The
disease-related indicators are subsequently utilised as input for the indicator
expansion module, incorporating the "data-text-data" strategy. The
transformer-based generator then leverages these extracted features along with
image features as auxiliary information to generate final reports. Furthermore,
the proposed IIHT method is feasible for radiologists to modify disease
indicators in real-world scenarios and integrate the operations into the
indicator expansion module for fluent and accurate medical report generation.
Extensive experiments and comparisons with state-of-the-art methods under
various evaluation metrics demonstrate the great performance of the proposed
method.