Generating Mammography Reports from Multi-view Mammograms with BERT
Alexander Yalunin, Elena Sokolova, Ilya Burenko, Alexander Ponomarchuk, Olga Puchkova, Dmitriy Umerenkov
Abstract
Writing mammography reports can be error-prone and time-consuming for radiologists. In this paper we propose a method to generate mammography reports given four images, corresponding to the four views used in screening mammography. To the best of our knowledge our work represents the first attempt to generate the mammography report using deep-learning. We propose an encoder-decoder model that includes an EfficientNet-based encoder and a Transformer-based decoder. We demonstrate that the Transformer-based attention mechanism can combine visual and semantic information to localize salient regions on the input mammograms and generate a visually interpretable report. The conducted experiments, including an evaluation by a certified radiologist, show the effectiveness of the proposed method.- Anthology ID:
- 2021.findings-emnlp.15
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 153–162
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.15
- DOI:
- 10.18653/v1/2021.findings-emnlp.15
- Cite (ACL):
- Alexander Yalunin, Elena Sokolova, Ilya Burenko, Alexander Ponomarchuk, Olga Puchkova, and Dmitriy Umerenkov. 2021. Generating Mammography Reports from Multi-view Mammograms with BERT. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 153–162, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Generating Mammography Reports from Multi-view Mammograms with BERT (Yalunin et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2021.findings-emnlp.15.pdf