Search Results for author: Farhan Akram

Found 9 papers, 4 papers with code

Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation

no code implementations5 Jul 2019 Farhan Akram, Vivek Kumar Singh, Hatem A. Rashwan, Mohamed Abdel-Nasser, Md. Mostafa Kamal Sarker, Nidhi Pandey, Domenec Puig

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.

SLSNet: Skin lesion segmentation using a lightweight generative adversarial network

1 code implementation1 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.

Image Segmentation Lesion Segmentation +2

An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning

no code implementations1 Jul 2019 Vivek Kumar Singh, Hatem A. Rashwan, Mohamed Abdel-Nasser, Md. Mostafa Kamal Sarker, Farhan Akram, Nidhi Pandey, Santiago Romani, Domenec Puig

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.

General Classification SSIM +1

Fence GAN: Towards Better Anomaly Detection

2 code implementations2 Apr 2019 Cuong Phuc Ngo, Amadeus Aristo Winarto, Connie Kou Khor Li, Sojeong Park, Farhan Akram, Hwee Kuan Lee

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.

Anomaly Classification Anomaly Detection

Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network

no code implementations11 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.

Image Segmentation Optic Disc Segmentation +1

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