S
Santiago Romani
Researcher at Rovira i Virgili University
Publications - 18
Citations - 574
Santiago Romani is an academic researcher from Rovira i Virgili University. The author has contributed to research in topics: Convolutional neural network & Segmentation. The author has an hindex of 9, co-authored 16 publications receiving 309 citations.
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
FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention
Vivek Kumar Singh,Mohamed Abdel-Nasser,Hatem A. Rashwan,Farhan Akram,Nidhi Pandey,Alain Lalande,Benoit Presles,Santiago Romani,Domenec Puig +8 more
TL;DR: This paper introduces a new block in the encoder of cGAN called factorized channel attention (FCA), which exploits both channel attention mechanism and residual 1-D kernel factorized convolution to improve discriminability between the lesion and non-lesion features.