The X-Raydar study is described in the following manuscript:
- Deep learning for comprehensive detection of abnormalities on chest X-rays: findings and tools from a retrospective multicenter study. Under review
The following are related publications from our group:
- Annarumma, M., Withey, S. J., Bakewell, R. J., Pesce, E., Goh, V., Montana, G. (2019).
Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology, 291(1), 196.
- Pesce, E., Withey, S. J., Ypsilantis, P. P., Bakewell, R., Goh, V., & Montana, G. (2019).
Learning to detect chest radiographs containing pulmonary lesions using visual attention networks. Medical image analysis, 53, 26-38.
- Santeramo, R., Withey, S., & Montana, G. (2018).
Longitudinal detection of radiological abnormalities with time-modulated LSTM. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 326-333). Springer, Cham.
- Ypsilantis, P. P., & Montana, G. (2017).
Learning what to look in chest X-rays with a recurrent visual attention model. arXiv preprint arXiv:1701.06452.
- Cornegruta, S., Bakewell, R., Withey, S., & Montana, G. (2016).
Modelling radiological language with bidirectional long short-term memory networks. arXiv preprint arXiv:1609.08409.