All Outputs (4)

Brain MR image segmentation using multiphase active contours based on local and global fitted images. (2018)
Conference Proceeding
AKRAM, F., SINGH, V.K., SARKER, M.M.K., GARCIA, M.A. and PUIG, D. 2018. Brain MR image segmentation using multiphase active contours based on local and global fitter images. In Falomir, Z., Gilbert, K. and Plaza, E. (eds.). Artificial intelligence research and development: current challenges, new trends and applications; contributions from 21st international conference of Catalan Association for Artificial Intelligence 2018 (CCIA 2018), 8-10 October 2018, Alt Empordà, Spain. Frontiers in artificial intelligence and applications, 308. Amsterdam: IOP Press [online], pages 325-334. Available from: https://doi.org/10.3233/978-1-61499-918-8-325

The study of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) regions in the brain magnetic resonance (MR) images can be useful for determining different brain disorders, assisting brain surgery, post-surgical analysis, saliency dete... Read More about Brain MR image segmentation using multiphase active contours based on local and global fitted images..

Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. (2018)
Conference Proceeding
SINGH, V.K., ROMANI, S., RASHWAN, H.A., AKRAM, F., PANDEY, N., SARKER, M.M.K., ABDULWAHAB, S., TORRENTS-BARRENA, J., SALEH, A., ARQUEZ, M., ARENAS, M. and PUIG, D. 2018. Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. In Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C. and Fictinger, G. (eds.) Medical image computing and computer assisted intervention (MICCAI 2018): proceedings of 21st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018), 16-20 September 2018, Granada, Spain. Lecture notes in computer science, 11071. Cham: Springer [online], pages 833-840. Available from: https://doi.org/10.1007/978-3-030-00934-2_92

This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structure is well-suited to accurately outline the mass area, especially when... Read More about Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification..

SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks (2018)
Conference Proceeding
SARKER, M.M.K., RASHWAN, H.A., AKRAM, F., BANU, S.F., SALEH, A., SINGH, V.K., CHOWDHURY, F.U.H., ABDULWAHAB, S., ROMANI, S., RADEVA, P. and PUIG, D. 2018. SLSDeep: skin lesion segmentation based on dilated residual and pyramid pooling networks. In Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C. and Fictinger, G. (eds.) Medical image computing and computer assisted intervention (MICCAI 2018): proceedings of 21st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018), 16-20 September 2018, Granada, Spain. Lecture notes in computer science, 11071. Cham: Springer [online], pages 21-29. Available from: https://doi.org/10.1007/978-3-030-00934-2_3

Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model represented as an encoder-decoder network. The encoder network is constructed by di... Read More about SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks.

Deep visual embedding for image classification. (2018)
Conference Proceeding
SALEH, A., ABDEL-NASSER, M., SARKER, M.M.K., SINGH, V.K., ABDULWAHAB, S., SAFFARI, N., GARCIA, M.A. and PUIG, D. 2018. Deep visual embedding for image classification. In Proceedings of 2018 international conference on Innovative trends in computer engineering (ITCE 2018), 19-21 February 2018, Aswan, Egypt. Piscataway: IEEE [online], pages 31-35. Available from: https://doi.org/10.1109/ITCE.2018.8316596

This paper proposes a new visual embedding method for image classification. It goes further in the analogy with textual data and allows us to read visual sentences in a certain order as in the case of text. The proposed method considers the spatial r... Read More about Deep visual embedding for image classification..