scholarly journals From medical image computing to computer-aided intervention: development of a research interface for image-guided navigation

2009 ◽  
Vol 5 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Xenophon Papademetris ◽  
Christine DeLorenzo ◽  
Sven Flossmann ◽  
Markus Neff ◽  
Kenneth P. Vives ◽  
...  
Keyword(s):  
Medical Image ◽  
Intervention Development ◽  
Image Guided ◽  
Computer Aided ◽  
Medical Image Computing

scholarly journals Medical Image Computing and Computer-Aided Medical Interventions Applied to Soft Tissues: Work in Progress in Urology

2006 ◽  
Vol 94 (9) ◽  
pp. 1665-1677 ◽  
Author(s):  
J. Troccaz ◽  
M. Baumann ◽  
P. Berkelman ◽  
P. Cinquin ◽  
V. Daanen ◽  
...  
Keyword(s):  
Medical Image ◽  
Soft Tissues ◽  
Medical Interventions ◽  
Work In Progress ◽  
Computer Aided ◽  
Medical Image Computing

scholarly journals Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

2020 ◽  
Vol 117 (23) ◽  
pp. 12592-12594 ◽  
Author(s):  
Agostina J. Larrazabal ◽  
Nicolás Nieto ◽  
Victoria Peterson ◽  
Diego H. Milone ◽  
Enzo Ferrante
Keyword(s):  
Medical Imaging ◽  
Medical Image ◽  
Computer Aided Diagnosis ◽  
Computer Assisted ◽  
Gender Imbalance ◽  
Gender Balance ◽  
Computer Aided ◽  
Medical Image Computing ◽  
Assisted Diagnosis ◽  
Aided Diagnosis

Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research community of medical image computing is making great efforts in developing more accurate algorithms to assist medical doctors in the difficult task of disease diagnosis. However, little attention is paid to the way databases are collected and how this may influence the performance of AI systems. Our study sheds light on the importance of gender balance in medical imaging datasets used to train AI systems for computer-assisted diagnosis. We provide empirical evidence supported by a large-scale study, based on three deep neural network architectures and two well-known publicly available X-ray image datasets used to diagnose various thoracic diseases under different gender imbalance conditions. We found a consistent decrease in performance for underrepresented genders when a minimum balance is not fulfilled. This raises the alarm for national agencies in charge of regulating and approving computer-assisted diagnosis systems, which should include explicit gender balance and diversity recommendations. We also establish an open problem for the academic medical image computing community which needs to be addressed by novel algorithms endowed with robustness to gender imbalance.

MICCAI: medical image computing and computer-assisted intervention1

2003 ◽  
Vol 10 (12) ◽  
pp. 1339-1340 ◽  
Author(s):  
Yoshinobu Sato ◽  
Guido Gerig
Keyword(s):  
Medical Image ◽  
Computer Assisted ◽  
Medical Image Computing

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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