H
Hatem A. Rashwan
Researcher at Rovira i Virgili University
Publications - 75
Citations - 1104
Hatem A. Rashwan is an academic researcher from Rovira i Virgili University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 14, co-authored 61 publications receiving 657 citations. Previous affiliations of Hatem A. Rashwan include ENSEEIHT & University of Toulouse.
Papers
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Journal ArticleDOI
Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network
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 +10 more
TL;DR: In this paper, a conditional Generative Adversarial Network (cGAN) was proposed to segment a breast tumor within a region of interest (ROI) in a mammogram.
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Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network
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 +10 more
TL;DR: A conditional Generative Adversarial Network (cGAN) devised to segment a breast mass within a region of interest (ROI) in a mammogram outperforms several state-of-the-art approaches.
Book ChapterDOI
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks
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 +10 more
TL;DR: A robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network, which outperforms the state-of-the-art methods in terms of segmentation accuracy.
Posted Content
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks
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 +10 more
TL;DR: Wang et al. as mentioned in this paper presented a robust deep learning skin lesion segmentation (SLSDeep) model, which is represented as an encoder-decoder network, where the encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder.
Journal ArticleDOI
Illumination-Robust Optical Flow Using a Local Directional Pattern
TL;DR: This paper proposes an illumination-robust constancy based on a robust texture descriptor rather than the brightness constancy, which will yield state-of-the-art results on the KITTI, Midleburry, and MPI-sintel data sets.