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Farhan Akram
Agency for Science, Technology and Research
30Publications
7H-index
108Citations
Publications 33
Newest
Abstract Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast tumors, which portray crucial morphological information that will support reliable diagnosis. In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast tumor within a region of interest (ROI) in a mammogram. The generative network learns ...
3 CitationsSource
#1Ziying Vanessa Lim (National Skin Centre)H-Index: 1
#2Farhan Akram (Agency for Science, Technology and Research)H-Index: 7
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#1Md. Mostafa Kamal Sarker (URV: Rovira i Virgili University)H-Index: 4
#2Hatem A. RashwanH-Index: 8
Last.Domenec PuigH-Index: 16
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Skin lesion segmentation in dermoscopic images is a challenge due to their blurry and irregular boundaries. Most of the segmentation approaches based on deep learning are time and memory consuming due to the hundreds of millions of parameters. Consequently, it is difficult to apply them to real dermatoscope devices with limited GPU and memory resources. In this paper, we propose a lightweight and efficient Generative Adversarial Networks (GAN) model, called MobileGAN for skin lesion segmentation...
#1Vivek Kumar SinghH-Index: 2
Last.Domenec PuigH-Index: 2
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#1Vivek Kumar Singh (URV: Rovira i Virgili University)H-Index: 2
#2Hatem A. RashwanH-Index: 8
Last.Domenec PuigH-Index: 16
view all 8 authors...
This paper proposes an efficient solution for tumor segmentation and classification in breast ultrasound (BUS) images. 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. To automatically re-balance the relative impact of each of the highest level encoded features, we also propose to add a channel-wise weighting block in the network. In addition, the SSIM and L1-n...
#2Hatem A. RashwanH-Index: 8
Last.Domenec PuigH-Index: 16
view all 7 authors...
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#1Vivek Kumar Singh (URV: Rovira i Virgili University)H-Index: 2
#2Vivek SinghH-Index: 28
Last.Domenec PuigH-Index: 16
view all 9 authors...
Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this paper, we propose an accurate skin lesion segmentation model based on a modified conditional generative adversarial network (cGAN). We introduce a new block in the encoder of cGAN called factorized channel attention (FCA), which exploits both channel attention mechanism and resi...
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Sep 16, 2018 in MICCAI (Medical Image Computing and Computer-Assisted Intervention)
#1Vivek SinghH-Index: 28
#2Santiago RomaniH-Index: 3
Last.Domenec PuigH-Index: 16
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This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structure is well-suited to accurately outline the mass area, especially when the training data is limited. The generative network learns intrinsic features of tumors while the adversarial network enforces segmentations to be similar to the ground truth. Experiments performed on dozens of malignant tumors extracted from the pu...
4 CitationsSource
This paper proposed a retinal image segmentation method based on conditional Generative Adversarial Network (cGAN) to segment optic disc. The proposed model consists of two successive networks: generator and discriminator. The generator learns to map information from the observing input (i.e., retinal fundus color image), to the output (i.e., binary mask). Then, the discriminator learns as a loss function to train this mapping by comparing the ground-truth and the predicted output with observing...
2 Citations
Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. 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. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by com...
12 Citations
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