Hybrid framework for automated generation of mammography radiology reports

Breast cancer remains a significant health concern for women at various stages of life, impacting both productivity and reproductive health. Recent advancements in deep learning (DL) have enabled substantial progress in the automation of radiological reports, offering potential support to radiologists and streamlining examination processes. This study introduces a framework for automated clinical text generation aimed at assisting radiologists in mammography examinations. Rather than replacing medical expertise, the system provides pre-processed evidence and automatic diagnostic suggestions for radiologist validation. The framework leverages an encoder-decoder architecture for natural language generation (NLG) models, trained and fine-tuned on a corpus of Spanish radiological text. Additionally, we incorporate an image intensity enhancement technique to address the issue of image quality variability and assess its impact on report generation outcomes. A comparative analysis using NLG metrics is conducted to identify the optimal feature extraction method. Furthermore, named entity recognition (NER) techniques are employed to extract key clinical concepts and automate precision evaluations. Our results demonstrate that the proposed framework could be a solid starting point for systematizing and implementing automated clinical report generation based on medical images.