scholarly journals Computer Aided Diagnosis - Medical Image Analysis Techniques

2018 ◽  
Author(s):  
Bhagirathi Halalli ◽  
Aziz Makandar
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Computer Aided Diagnosis ◽  
Analysis Techniques ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Image Analysis Techniques

Medical image analysis: computer-aided diagnosis of gastric cancer invasion on endoscopic images

2011 ◽  
Vol 26 (5) ◽  
pp. 1485-1489 ◽  
Author(s):  
Keisuke Kubota ◽  
Junko Kuroda ◽  
Masashi Yoshida ◽  
Keiichiro Ohta ◽  
Masaki Kitajima
Keyword(s):  
Gastric Cancer ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Cancer Invasion ◽  
Computer Aided Diagnosis ◽  
Endoscopic Images ◽  
Computer Aided ◽  
Analysis Computer ◽  
Aided Diagnosis

Special Issue on Medical Image Analysis for Computer Aided Diagnosis

IRBM ◽  
2016 ◽  
Vol 37 (1) ◽  
pp. 1-2
Author(s):  
K. Djemal ◽  
S. Ruan ◽  
C. Toumoulin
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Computer Aided Diagnosis ◽  
Special Issue ◽  
Computer Aided ◽  
Aided Diagnosis

Call for papers for a Special Issue on Medical Image Analysis for Computer Aided Diagnosis

IRBM ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 3
Author(s):  
Khalifa Djemal ◽  
Su Ruan ◽  
Christine Toumoulin
Keyword(s):  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Computer Aided Diagnosis ◽  
Special Issue ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals MAGAN: Mask Attention Generative Adversarial Network for Liver Tumor CT Image Synthesis

2020 ◽  
Author(s):  
Yang Liu ◽  
Lu Meng ◽  
Jianping Zhong
Keyword(s):  
Liver Tumor ◽  
Medical Image ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Generative Adversarial Network ◽  
Liver Ct ◽  
Adversarial Network ◽  
Computer Aided ◽  
Image Dataset ◽  
Aided Diagnosis

Abstract Background: For deep learning, the size of the dataset greatly affects the final training effect. However, in the field of computer-aided diagnosis, medical image datasets are often limited and even scarce.Methods: We aim to synthesize medical images and enlarge the size of the medical image dataset. In the present study, we synthesized the liver CT images with a tumor based on the mask attention generative adversarial network (MAGAN). We masked the pixels of the liver tumor in the image as the attention map. And both the original image and attention map were loaded into the generator network to obtain the synthesized images. Then the original images, the attention map, and the synthesized images were all loaded into the discriminator network to determine if the synthesized images were real or fake. Finally, we can use the generator network to synthesize liver CT images with a tumor.Results: The experiments showed that our method outperformed the other state-of-the-art methods, and can achieve a mean peak signal-to-noise ratio (PSNR) as 64.72dB.Conclusions: All these results indicated that our method can synthesize liver CT images with tumor, and build large medical image dataset, which may facilitate the progress of medical image analysis and computer-aided diagnosis.

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