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Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images. (2020)
Journal Article
SARKER, M.M.K., MAKHLOUF, Y., BANU, S.F., CHAMBON, S., RADEVA, P. and PUIG, D. 2020. Web-based efficient dual attention networks to detect COVID-19 from X-ray images. Electronics letters [online], 56(24), pages 1298-1301. Available from: https://doi.org/10.1049/el.2020.1962

Rapid and accurate detection of COVID-19 is a crucial step to control the virus. For this purpose, the authors designed a web-based COVID-19 detector using efficient dual attention networks, called ‘EDANet’. The EDANet architecture is based on invert... Read More about Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images..

Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework. (2020)
Journal Article
SINGH, V.K., ABDEL-NASSER, M., AKRAM, F., RASHWAN, H.A., SARKER, M.M.K., PANDEY, N., ROMANI, S. and PUIG, D. 2020. Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework. Expert systems with applications [online], 162, article 113870. Available from: https://doi.org/10.1016/j.eswa.2020.113870

Automatic tumor segmentation in breast ultrasound (BUS) images is still a challenging task because of many sources of uncertainty, such as speckle noise, very low signal-to-noise ratio, shadows that make the anatomical boundaries of tumors ambiguous,... Read More about Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework..