Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Akram, Farhan"'
Autor:
Zhu, Chuang, Liu, Shengjie, Yu, Zekuan, Xu, Feng, Aggarwal, Arpit, Corredor, Germán, Madabhushi, Anant, Qu, Qixun, Fan, Hongwei, Li, Fangda, Li, Yueheng, Guan, Xianchao, Zhang, Yongbing, Singh, Vivek Kumar, Akram, Farhan, Sarker, Md. Mostafa Kamal, Shi, Zhongyue, Jin, Mulan
For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving m
Externí odkaz:
http://arxiv.org/abs/2305.03546
Autor:
Akram, Farhan
Discrete silicon carbide (SiC) power devices have unique characteristics that outpace those of silicon (Si) counterparts. The improved physical features have provided better faster switching, greater current densities, lower on-resistance, and temper
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-41188
Autor:
Akram, Farhan, Singh, Vivek Kumar, Rashwan, Hatem A., Abdel-Nasser, Mohamed, Sarker, Md. Mostafa Kamal, Pandey, Nidhi, Puig, Domenec
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. In the generator network of the adversarial framework, spatial pyramid poo
Externí odkaz:
http://arxiv.org/abs/1907.02742
Autor:
Singh, Vivek Kumar, Rashwan, Hatem A., Abdel-Nasser, Mohamed, Sarker, Md. Mostafa Kamal, Akram, Farhan, Pandey, Nidhi, Romani, Santiago, Puig, Domenec
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
Externí odkaz:
http://arxiv.org/abs/1907.00887
Autor:
Sarker, Md. Mostafa Kamal, Rashwan, Hatem A., Akram, Farhan, Singh, Vivek Kumar, Banu, Syeda Furruka, Chowdhury, Forhad U H, Choudhury, Kabir Ahmed, Chambon, Sylvie, Radeva, Petia, Puig, Domenec, Abdel-Nasser, Mohamed
The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the
Externí odkaz:
http://arxiv.org/abs/1907.00856
Autor:
Ngo, Cuong Phuc, Winarto, Amadeus Aristo, Li, Connie Kou Khor, Park, Sojeong, Akram, Farhan, Lee, Hwee Kuan
Anomaly detection is a classical problem where the aim is to detect anomalous data that do not belong to the normal data distribution. Current state-of-the-art methods for anomaly detection on complex high-dimensional data are based on the generative
Externí odkaz:
http://arxiv.org/abs/1904.01209
Autor:
Singh, Vivek Kumar, Rashwan, Hatem A., Romani, Santiago, Akram, Farhan, Pandey, Nidhi, Sarker, Md. Mostafa Kamal, Saleh, Adel, Arenas, Meritexell, Arquez, Miguel, Puig, Domenec, Torrents-Barrena, Jordina
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 masses, which portray crucial morphological in
Externí odkaz:
http://arxiv.org/abs/1809.01687
Autor:
Singh, Vivek Kumar, Rashwan, Hatem A., Saleh, Adel, Akram, Farhan, Sarker, Md Mostafa Kamal, Pandey, Nidhi, Abdulwahab, Saddam
In this paper, an optic disc and cup segmentation method is proposed using U-Net followed by a multi-scale feature matching network. The proposed method targets task 2 of the REFUGE challenge 2018. In order to solve the segmentation problem of task 2
Externí odkaz:
http://arxiv.org/abs/1807.11433
Autor:
Singh, Vivek Kumar, Rashwan, Hatem, Akram, Farhan, Pandey, Nidhi, Sarker, Md. Mostaf Kamal, Saleh, Adel, Abdulwahab, Saddam, Maaroof, Najlaa, Romani, Santiago, Puig, Domenec
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
Externí odkaz:
http://arxiv.org/abs/1806.03905
Autor:
Sarker, Md. Mostafa Kamal, Rashwan, Hatem A., Akram, Farhan, Banu, Syeda Furruka, Saleh, Adel, Singh, Vivek Kumar, Chowdhury, Forhad U H, Abdulwahab, Saddam, Romani, Santiago, Radeva, Petia, Puig, Domenec
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
Externí odkaz:
http://arxiv.org/abs/1805.10241