In this paper, we propose an efficient blood vessel segmentation method for the eye fundus images using adversarial learning with multiscale features and kernel factorization.
We propose to add an atrous convolution layer to the conditional generative adversarial network (cGAN) segmentation model to learn tumor features at different resolutions of BUS images.
1 code implementation • 5 Sep 2018 • Vivek Kumar Singh, Hatem A. Rashwan, Santiago Romani, Farhan Akram, Nidhi Pandey, Md. Mostafa Kamal Sarker, Adel Saleh, Meritexell Arenas, Miguel Arquez, Domenec Puig, Jordina Torrents-Barrena
In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast mass within a region of interest (ROI) in a mammogram.
In this paper, an optic disc and cup segmentation method is proposed using U-Net followed by a multi-scale feature matching network.
no code implementations • 11 Jun 2018 • Vivek Kumar Singh, Hatem Rashwan, Farhan Akram, Nidhi Pandey, Md. Mostaf Kamal Sarker, Adel Saleh, Saddam Abdulwahab, Najlaa Maaroof, Santiago Romani, Domenec Puig
Then, the discriminator learns as a loss function to train this mapping by comparing the ground-truth and the predicted output with observing the input image as a condition. Experiments were performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE.
2 code implementations • 25 May 2018 • Vivek Kumar Singh, Santiago Romani, Hatem A. Rashwan, Farhan Akram, Nidhi Pandey, Md. Mostafa Kamal Sarker, Jordina Torrents Barrena, Saddam Abdulwahab, Adel Saleh, Miguel Arquez, Meritxell Arenas, Domenec Puig
This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography.