A Survey on Deep Learning and Explainability for Automatic Image-based Medical Report Generation. Messina, P., Pino, P., Parra, D., Soto, A., Besa, C., Uribe, S., Andía, M., Tejos, C., Prieto, C., & Capurro, D. arxiv, 2020.
A Survey on Deep Learning and Explainability for Automatic Image-based Medical Report Generation [link]Paper  abstract   bibtex   
Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to: (1) Datasets, (2) Architecture Design, (3) Explainability and (4) Evaluation Metrics. Our survey identifies interesting developments, but also remaining challenges. Among them, the current evaluation of generated reports is especially weak, since it mostly relies on traditional Natural Language Processing (NLP) metrics, which do not accurately capture medical correctness.
@article{CarvalloEtAl:2020,
  Author = {P. Messina and P. Pino and D. Parra and A. Soto and C. Besa and S. Uribe and M. Andía and C. Tejos and C. Prieto and D. Capurro},
  Title = {A Survey on Deep Learning and Explainability for Automatic Image-based Medical Report Generation},
  Journal = {arxiv},
  Year = {2020},
  abstract = {Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to: (1) Datasets, (2) Architecture Design, (3) Explainability and (4) Evaluation Metrics. Our survey identifies interesting developments, but also remaining challenges. Among them, the current evaluation of generated reports is especially weak, since it mostly relies on traditional Natural Language Processing (NLP) metrics, which do not accurately capture medical correctness.},
url = {https://arxiv.org/abs/2010.10563}
}

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