scholarly journals Unsupervised Learning for Cross-Domain Medical Image Synthesis Using Deformation Invariant Cycle Consistency Networks

2018 ◽  
pp. 52-60 ◽  
Author(s):  
Chengjia Wang ◽  
Gillian Macnaught ◽  
Giorgos Papanastasiou ◽  
Tom MacGillivray ◽  
David Newby
Keyword(s):  
Unsupervised Learning ◽  
Medical Image ◽  
Image Synthesis ◽  
Cross Domain

TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis

2019 ◽  
pp. 245-254 ◽  
Author(s):  
Chengjia Wang ◽  
Giorgos Papanastasiou ◽  
Sotirios Tsaftaris ◽  
Guang Yang ◽  
Calum Gray ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Synthesis ◽  
Cross Domain

scholarly journals DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis

2021 ◽  
Vol 67 ◽  
pp. 147-160
Author(s):  
Chengjia Wang ◽  
Guang Yang ◽  
Giorgos Papanastasiou ◽  
Sotirios A. Tsaftaris ◽  
David E. Newby ◽  
...  
Keyword(s):  
Information Fusion ◽  
Medical Image ◽  
Image Synthesis ◽  
Cross Domain ◽  
Domain Information

scholarly journals Medical image processing with contextual style transfer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yin Xu ◽  
Yan Li ◽  
Byeong-Seok Shin
Keyword(s):  
Medical Image ◽  
Medical Image Processing ◽  
Image Synthesis ◽  
Industrial Applications ◽  
Generative Models ◽  
Context Aware ◽  
Training Set ◽  
Style Transfer ◽  
Segmentation Accuracy ◽  
Generative Methods

Abstract With recent advances in deep learning research, generative models have achieved great achievements and play an increasingly important role in current industrial applications. At the same time, technologies derived from generative methods are also under a wide discussion with researches, such as style transfer, image synthesis and so on. In this work, we treat generative methods as a possible solution to medical image augmentation. We proposed a context-aware generative framework, which can successfully change the gray scale of CT scans but almost without any semantic loss. By producing target images that with specific style / distribution, we greatly increased the robustness of segmentation model after adding generations into training set. Besides, we improved 2– 4% pixel segmentation accuracy over original U-NET in terms of spine segmentation. Lastly, we compared generations produced by networks when using different feature extractors (Vgg, ResNet and DenseNet) and made a detailed analysis on their performances over style transfer.

A survey of unsupervised learning in medical image registration

2022 ◽  
Vol 2 (1) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Unsupervised Learning ◽  
Medical Image ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Clinical Applications ◽  
Future Research ◽  
Medical Image Registration ◽  
Short Time

Medical image registration has important value in actual clinical applications. From the traditional time-consuming iterative similarity optimization method to the short time-consuming supervised deep learning to today's unsupervised learning, the continuous optimization of the registration strategy makes it more feasible in clinical applications. This survey mainly focuses on unsupervised learning methods and introduces the latest solutions for different registration relationships. The registration for inter-modality is a more challenging topic. The application of unsupervised learning in registration for inter-modality is the focus of this article. In addition, this survey also proposes ideas for future research methods to show directions of the future research.

scholarly journals Review of Breast Cancer Pathologigcal Image Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ya-nan Zhang ◽  
Ke-rui XIA ◽  
Chang-yi LI ◽  
Ben-li WEI ◽  
Bing Zhang
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Unsupervised Learning ◽  
Medical Image ◽  
Breast Cancer Diagnosis ◽  
Breast Cancer Patients ◽  
Medical Image Diagnosis ◽  
Important Means ◽  
Segmentation Image ◽  
Pathological Image

Breast cancer is one of the most common malignancies. Pathological image processing of breast has become an important means for early diagnosis of breast cancer. Using medical image processing to assist doctors to detect potential breast cancer as early as possible has always been a hot topic in the field of medical image diagnosis. In this paper, a breast cancer recognition method based on image processing is systematically expounded from four aspects: breast cancer detection, image segmentation, image registration, and image fusion. The achievements and application scope of supervised learning, unsupervised learning, deep learning, CNN, and so on in breast cancer examination are expounded. The prospect of unsupervised learning and transfer learning for breast cancer diagnosis is prospected. Finally, the privacy protection of breast cancer patients is put forward.

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