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12 avril 2019

Automatic Medical Report Generation for Medical Images

Catégorie : Doctorant

Investigate a new hybrid deep learning architecture for automatic generation of medical report. The project will consist in developing a multi-level multi attention (MLMA) architecture with the combination of CNN (extracting visual features from the original image), Long Short Term Memory (LSTM) and Bidirectional-LSTM for the generation of radiological report given chest x-ray images. The combination of context level visual attention and textual attention should ensure MLMA model to learn the syntactical and structural pattern, which should sequentially generate a plausible medical report. The work will be first concentrated on chest X-rays medical images for which image databases and associated reports are available on-line ; later on, a possible extension could be in the field of retinal disease, cardiology (from MRI inputs, new challenge for the image input!) or digital mammography through established partnerships and collaborations.

Associated Litterature:

Jing, B., Xie, P., Xing, E.: On the automatic generation of medical imaging reports. arXiv preprint arXiv:1711.08195 (2017).

Krause, J., Johnson, J., Krishna, R., Fei-Fei, L.: A hierarchical approach for generating descriptive image paragraphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 317-325 (2017).

Wang, X., Peng, Y., Lu, L., Lu, Z., Summers, R.M.: Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9049-9058 (2018).

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y.: Show, attend and tell: Neural image caption generation with visual attention. In: International Conference on Machine Learning. pp. 2048-2057 (2015)



Application deadline Mai 15 - 2019.

Grant type: Bourse Ministére, around 1500 Euros/month for 3 years.

Starting date: Mid september 2019.



ImvIa Research Laboratory

Université de Bourgogne




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