EnGAN: Enhancement Generative Adversarial Network in Medical Image Segmentation

Deep learning has been widely used in medical image segmentation, although the accuracy is affected by the problems of small sample space, data imbalance, and cross-device differences. Aiming at such issues, a enhancement GAN network is proposed by using the domain transferring of the adversarial generation network to enhance the original medical images. Specifically, based on retaining the transferability of the original GAN network, a new optimizer is added to generate a sample space with a continuous distribution, which can be used as the target domain of the original image transferring. The optimizer back-propagates the labels of the supervised data set through the segmentation network and maps the discrete distribution of the labels to the continuous image distribution, which has a high similarity to the original image but improves the segmentation efficiency.On this basis, the optimized distribution is taken as the target domain, and the generator and discriminator of the GAN network are trained so that the generator can transfer the original image distribution to the target distribution. extensive experiments are conducted based on MRI, CT, and ultrasound data sets. The experimental results show that, the proposed method has a good generalization effect in medical image segmentation, even when the data set has limited sample space and data imbalance to a certain extent.

A Novel Method for Handicrafts Design Based on Fusion of Multi-Intelligent Decision Algorithm

With the rapid development of artificial intelligence, handicraft design has developed from artificial design to artificial intelligence design. Traditional handicraft design has the problems of long time consumption and low output, so it is necessary to improve the process technology. Artificial intelligence technology can provide optimized design steps in handicraft design and improve design efficiency and process level. Handicrafts are regarded as important social products and exist in people’s daily life. In the current society, many people do handicrafts and there are major exhibitions. Furthermore, the display of handicrafts is also very grand and shocking. In the design of handicrafts, the traditional design method cannot completely keep up with the production speed and efficiency of handicrafts. Therefore, this paper adopts the fusion multi-intelligent decision algorithm of multi-node branch design in the design method of handicraft. The algorithm model combination is used to analyze and design the layout of the handicraft, which speeds up the design efficiency and production of the handicraft. In this paper, two intelligent algorithms will be used for fusion; they are genetic algorithm and GA-PSO fusion algorithm obtained by particle swarm optimization and they are embedded in handicraft design method for application through mathematical model construction and function construction. After comparing the performance parameter index data of three intelligent algorithms and GA-PSO fusion algorithm, it is obtained that GA-PSO fusion algorithm is 97% correct and has 82% readability, 72% robustness, and 61% structure, making it have better important indicators. Four algorithms optimize each design problem in all aspects of handicraft design at present. Design efficiency, image distribution rate, image optimization degree, and image clarity are compared by simulation experiments. Compared with three intelligent algorithms, traditional design methods, and manual design methods, GA-PSO fusion algorithm can effectively improve the design method and design effect of handicrafts with 92.1% design efficiency, 82.7% image distribution rate, 94.3% image optimization degree, and 84% layout void rate. Finally, the space complexity experiment of four algorithms shows that GA-PSO algorithm can achieve 9.73 dispersion with 11.42 space complexities, which makes the dimension reduction relatively stable, and the algorithm can maintain stability in the design and application of handicrafts.

Rediscovery and extended distribution of Indigofera santapaui Sanjappa (Leguminosae: Papilionoideae) from the states of Maharashtra and Gujarat, India

Indigofera santapaui Sanjappa is a little known endemic legume described from the northern Western Ghats. It is rediscovered and collected from other localities quite away from the type locality. This is found to be the only known species of Indigofera with yellow flowers. A brief description, image, distribution, and ecological notes have been provided to facilitate correct identification and to know its extent of distribution.

‘Young People Just Resolve It in Their Own Group’: Young People’s Perspectives on Responses to Non-Consensual Intimate Image Distribution

While responses to non-consensual intimate image distribution (NCIID) often highlight criminal law remedies, little is known about how young people are choosing to respond to this act and whether they perceive legal intervention as a useful tool. Drawing from interviews with 10 teenagers and survey responses from 81 adult supporters, we provide insight into how young people perceive the supports available to them for responding to NCIID. We find young people may avoid seeking support from both the criminal justice system and adults in general due to fears of adult overreaction, victim blaming and shaming, and self/peer criminalization.

Image-quality assessment for full-disk solar observations with generative adversarial networks

Context. In recent decades, solar physics has entered the era of big data and the amount of data being constantly produced from ground- and space-based observatories can no longer be purely analyzed by human observers. Aims. In order to assure a stable series of recorded images of sufficient quality for further scientific analysis, an objective image-quality measure is required. Especially when dealing with ground-based observations, which are subject to varying seeing conditions and clouds, the quality assessment has to take multiple effects into account and provide information about the affected regions. The automatic and robust identification of quality-degrading effects is critical for maximizing the scientific return from the observations and to allow for event detections in real time. In this study, we develop a deep-learning method that is suited to identify anomalies and provide an image-quality assessment of solar full-disk Hα filtergrams. The approach is based on the structural appearance and the true image distribution of high-quality observations. Methods. We employ a neural network with an encoder–decoder architecture to perform an identity transformation of selected high-quality observations. The encoder network is used to achieve a compressed representation of the input data, which is reconstructed to the original by the decoder. We use adversarial training to recover truncated information based on the high-quality image distribution. When images of reduced quality are transformed, the reconstruction of unknown features (e.g., clouds, contrails, partial occultation) shows deviations from the original. This difference is used to quantify the quality of the observations and to identify the affected regions. In addition, we present an extension of this architecture that also uses low-quality samples in the training step. This approach takes characteristics of both quality domains into account, and improves the sensitivity for minor image-quality degradation. Results. We apply our method to full-disk Hα filtergrams from the Kanzelhöhe Observatory recorded during 2012−2019 and demonstrate its capability to perform a reliable image-quality assessment for various atmospheric conditions and instrumental effects. Our quality metric achieves an accuracy of 98.5% in distinguishing observations with quality-degrading effects from clear observations and provides a continuous quality measure which is in good agreement with the human perception. Conclusions. The developed method is capable of providing a reliable image-quality assessment in real time, without the requirement of reference observations. Our approach has the potential for further application to similar astrophysical observations and requires only coarse manual labeling of a small data set.

Visual and Quantitative Evaluation of Amyloid Brain PET Image Synthesis with Generative Adversarial Network

Conventional data augmentation (DA) techniques, which have been used to improve the performance of predictive models with a lack of balanced training data sets, entail an effort to define the proper repeating operation (e.g., rotation and mirroring) according to the target class distribution. Although DA using generative adversarial network (GAN) has the potential to overcome the disadvantages of conventional DA, there are not enough cases where this technique has been applied to medical images, and in particular, not enough cases where quantitative evaluation was used to determine whether the generated images had enough realism and diversity to be used for DA. In this study, we synthesized 18F-Florbetaben (FBB) images using CGAN. The generated images were evaluated using various measures, and we presented the state of the images and the similarity value of quantitative measurement that can be expected to successfully augment data from generated images for DA. The method includes (1) conditional WGAN-GP to learn the axial image distribution extracted from pre-processed 3D FBB images, (2) pre-trained DenseNet121 and model-agnostic metrics for visual and quantitative measurements of generated image distribution, and (3) a machine learning model for observing improvement in generalization performance by generated dataset. The Visual Turing test showed similarity in the descriptions of typical patterns of amyloid deposition for each of the generated images. However, differences in similarity and classification performance per axial level were observed, which did not agree with the visual evaluation. Experimental results demonstrated that quantitative measurements were able to detect the similarity between two distributions and observe mode collapse better than the Visual Turing test and t-SNE.