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.
1 code implementation • 1 Jul 2019 • Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Farhan Akram, Vivek Kumar Singh, Syeda Furruka Banu, Forhad U H Chowdhury, Kabir Ahmed Choudhury, Sylvie Chambon, Petia Radeva, Domenec Puig, Mohamed Abdel-Nasser
Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model.
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.
However, the traditional GAN loss is not directly aligned with the anomaly detection objective: it encourages the distribution of the generated samples to overlap with the real data and so the resulting discriminator has been found to be ineffective as an anomaly detector.
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.
no code implementations • 25 May 2018 • Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Farhan Akram, Syeda Furruka Banu, Adel Saleh, Vivek Kumar Singh, Forhad U H Chowdhury, Saddam Abdulwahab, Santiago Romani, Petia Radeva, Domenec Puig
The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge.
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.