Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network
- Mojtaba Akbari, Majid Mohrekesh, K. Najarian
- Computer Science, MedicineAnnual International Conference of the IEEE…
- 1 February 2018
Evaluation of the proposed polyp segmentation method using the CVC-ColonDB database shows that the proposed method achieves more accurate results in comparison with previous colonoscopy video-segmentation methods.
Skin lesion segmentation in clinical images using deep learning
- M. Jafari, N. Karimi, K. Najarian
- Computer ScienceInternational Conference on Pattern Recognition
- 1 December 2016
The experimental results show that the proposed method for accurate extraction of lesion region can outperform the existing state-of-the-art algorithms in terms of segmentation accuracy.
Vessel extraction in X-ray angiograms using deep learning
- E. Nasr-Esfahani, S. Samavi, K. Najarian
- Computer ScienceAnnual International Conference of the IEEE…
- 1 August 2016
Experimental results on angiography images of a dataset show that the proposed deep learning approach using convolutional neural networks has a superior performance in extraction of vessel regions.
Melanoma detection by analysis of clinical images using convolutional neural network.
- E. Nasr-Esfahani, S. Samavi, K. Najarian
- Computer ScienceConference proceedings
- 1 August 2016
Experimental results show that the proposed method for detection of melanoma lesions is superior in terms of diagnostic accuracy in comparison with the state-of-the-art methods.
Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network
- Mina Nasr-Esfahani, Majid Mohrekesh, K. Najarian
- Computer ScienceAnnual International Conference of the IEEE…
- 21 February 2018
This paper proposes an automated method for segmenting the left ventricle in cardiac MR images by first automatically extracting the region of interest and then employing it as an input of a fully convolutional network.
Melanoma detection by analysis of clinical images using convolutional neural network
- E. Nasr-Esfahani, S. Samavi, K. Najarian
- Computer ScienceAnnual International Conference of the IEEE…
- 1 August 2016
Experimental results show that the proposed method for detection of melanoma lesions is superior in terms of diagnostic accuracy in comparison with the state-of-the-art methods.
Segmentation of vessels in angiograms using convolutional neural networks
- E. Nasr-Esfahani, N. Karimi, K. Najarian
- Computer ScienceBiomedical Signal Processing and Control
- 1 February 2018
Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma
- M. Jafari, E. Nasr-Esfahani, N. Karimi, S. Soroushmehr, S. Samavi, K. Najarian
- MedicineInternational Journal of Computer Assisted…
- 24 March 2017
A new method based on deep neural networks is proposed for accurate extraction of a lesion region and can outperform other state-of-the-art algorithms exist in the literature.
Liver Segmentation in CT Images Using Three Dimensional to Two Dimensional Fully Convolutional Network
- Shima Rafiei, E. Nasr-Esfahani, S. Soroushmehr, N. Karimi, S. Samavi, K. Najarian
- MedicineInternational Conference on Information Photonics
- 21 February 2018
An efficient liver segmentation with the authors' 3D to 2D fully convolution network (3D-2D-FCN) is proposed, where the segmented mask is enhanced using the conditional random field on the organ's border.
Dense Fully Convolutional Network for Skin Lesion Segmentation
- E. Nasr-Esfahani, Shima Rafiee, K. Najarian
- Computer ScienceArXiv
- 29 December 2017
This paper proposes a new class of fully convolutional networks with novel dense pooling layers for segmentation of lesion regions in non-dermoscopic images and produces dice score of 91.6% which outperforms all state-of-the-art algorithms in segmentations of skin lesions based on the Dermquest dataset.
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