Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images

2020 ◽  
Author(s):  
Albert Swiecicki ◽  
Mateusz Buda ◽  
Ashirbani Saha ◽  
Nianyi Li ◽  
Sujata V. Ghate ◽  
...  
Keyword(s):  
Digital Breast Tomosynthesis ◽  
Image Completion ◽  
Generative Adversarial Network ◽  
Breast Tomosynthesis ◽  
Adversarial Network

scholarly journals A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Albert Swiecicki ◽  
Nicholas Konz ◽  
Mateusz Buda ◽  
Maciej A. Mazurowski
Keyword(s):  
Cancer Detection ◽  
Digital Breast Tomosynthesis ◽  
Generative Adversarial Network ◽  
Breast Tomosynthesis ◽  
Adversarial Network ◽  
Error Ratio ◽  
Duke University ◽  
Model Training ◽  
Bounding Boxes ◽  
Image Part

AbstractDeep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in cancer screening, such as digital breast tomosynthesis (DBT), where less than 1% of cases contain cancer. In this study, we propose a method to train an inpainting generative adversarial network to be used for cancer detection using only images that do not contain cancer. During inference, we removed a part of the image and used the network to complete the removed part. A significant error in completing an image part was considered an indication that such location is unexpected and thus abnormal. A large dataset of DBT images used in this study was collected at Duke University. It consisted of 19,230 reconstructed volumes from 4348 patients. Cancerous masses and architectural distortions were marked with bounding boxes by radiologists. Our experiments showed that the locations containing cancer were associated with a notably higher completion error than the non-cancer locations (mean error ratio of 2.77). All data used in this study has been made publicly available by the authors.

scholarly journals Image Completion with Large or Edge-Missing Areas

Algorithms ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
Jianjian Ji ◽  
Gang Yang
Keyword(s):  
Experimental Results ◽  
The Other ◽  
Context Information ◽  
Network Structures ◽  
Image Completion ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Other Hand ◽  
The One ◽  
Pseudo Color

Existing image completion methods are mostly based on missing regions that are small or located in the middle of the images. When regions to be completed are large or near the edge of the images, due to the lack of context information, the completion results tend to be blurred or distorted, and there will be a large blank area in the final results. In addition, the unstable training of the generative adversarial network is also prone to cause pseudo-color in the completion results. Aiming at the two above-mentioned problems, a method of image completion with large or edge-missing areas is proposed; also, the network structures have been improved. On the one hand, it overcomes the problem of lacking context information, which thereby ensures the reality of generated texture details; on the other hand, it suppresses the generation of pseudo-color, which guarantees the consistency of the whole image both in vision and content. The experimental results show that the proposed method achieves better completion results in completing large or edge-missing areas.

scholarly journals A Reference-Guided Double Pipeline Face Image Completion Network

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1969
Author(s):  
Hongrui Liu ◽  
Shuoshi Li ◽  
Hongquan Wang ◽  
Xinshan Zhu
Keyword(s):  
Signal To Noise Ratio ◽  
Receptive Fields ◽  
Similarity Index ◽  
Face Image ◽  
Reference Image ◽  
Image Completion ◽  
Generative Adversarial Network ◽  
Identity Information ◽  
Adversarial Network ◽  
Reference Images

The existing face image completion approaches cannot be utilized to rationally complete damaged face images where their identity information is completely lost due to being obscured by center masks. Hence, in this paper, a reference-guided double-pipeline face image completion network (RG-DP-FICN) is designed within the framework of the generative adversarial network (GAN) completing the identity information of damaged images utilizing reference images with the same identity as damaged images. To reasonably integrate the identity information of reference images into completed images, the reference image is decoupled into identity features (e.g., the contour of eyes, eyebrows, nose) and pose features (e.g., the orientation of face and the positions of the facial features), and then the resulting identity features are fused with posture features of damaged images. Specifically, a lightweight identity predictor is used to extract the pose features; an identity extraction module is designed to compress and globally extract the identity features of the reference images, and an identity transfer module is proposed to effectively fuse identity and pose features by performing identity rendering on different receptive fields. Furthermore, quantitative and qualitative evaluations are conducted on a public dataset CelebA-HQ. Compared to the state-of-the-art methods, the evaluation metrics peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and L1 loss are improved by 2.22 dB, 0.033 and 0.79%, respectively. The results indicate that RG-DP-FICN can generate completed images with reasonable identity, with superior completion effect compared to existing completion approaches.

Generative Image Inpainting with Dilated Deformable Convolution

2021 ◽  
Author(s):  
Zhao Qiu ◽  
Lin Yuan ◽  
Lihao Liu ◽  
Zheng Yuan ◽  
Tao Chen ◽  
...  
Keyword(s):  
Receptive Field ◽  
Image Inpainting ◽  
Image Generation ◽  
Image Completion ◽  
Generative Adversarial Network ◽  
Dilated Convolution ◽  
Adversarial Network ◽  
Image Library ◽  
Geometric Deformation ◽  
Stable Representation

The image generation and completion model complement the missing area of the image to be repaired according to the image itself or the information of the image library so that the repaired image looks very natural and difficult to distinguish from the undamaged image. The difficulty of image generation and completion lies in the reasonableness of image semantics and the clear and true texture of the generated image. In this paper, a Wasserstein generative adversarial network with dilated convolution and deformable convolution (DDC-WGAN) is proposed for image completion. A deformable offset is added based on dilated convolution, which enlarges the receptive field and provides a more stable representation of geometric deformation. Experiments show that the DDC-WGAN method proposed in this paper has better performance in image generation and complementation than the traditional generative adversarial complementation network.

scholarly journals Simultaneous Execution of Color Adjustment and Image Completion by Generative Adversarial Network

2018 ◽  
Vol 84 (12) ◽  
pp. 1033-1040
Author(s):  
Naofumi AKIMOTO ◽  
Masaki HAYASHI ◽  
Shuichi AKIZUKI ◽  
Yoshimitsu AOKI
Keyword(s):  
Image Completion ◽  
Generative Adversarial Network ◽  
Adversarial Network

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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