An image-computable model of human visual shape similarity

Shape is a defining feature of objects, and human observers can effortlessly compare shapes to determine how similar they are. Yet, to date, no image-computable model can predict how visually similar or different shapes appear. Such a model would be an invaluable tool for neuroscientists and could provide insights into computations underlying human shape perception. To address this need, we developed a model (‘ShapeComp’), based on over 100 shape features (e.g., area, compactness, Fourier descriptors). When trained to capture the variance in a database of >25,000 animal silhouettes, ShapeComp accurately predicts human shape similarity judgments between pairs of shapes without fitting any parameters to human data. To test the model, we created carefully selected arrays of complex novel shapes using a Generative Adversarial Network trained on the animal silhouettes, which we presented to observers in a wide range of tasks. Our findings show that incorporating multiple ShapeComp dimensions facilitates the prediction of human shape similarity across a small number of shapes, and also captures much of the variance in the multiple arrangements of many shapes. ShapeComp outperforms both conventional pixel-based metrics and state-of-the-art convolutional neural networks, and can also be used to generate perceptually uniform stimulus sets, making it a powerful tool for investigating shape and object representations in the human brain.

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An image computable model of visual shape similarity

Journal of Vision ◽

10.1167/19.10.37c ◽

2019 ◽

Vol 19 (10) ◽

pp. 37c

Author(s):

Yaniv Morgenstern ◽

Filipp Schmidt ◽

Frieder Hartmann ◽

Henning Tiedemann ◽

Eugen Prokott ◽

...

Keyword(s):

Shape Similarity ◽

Computable Model ◽

Visual Shape

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Adversarial generation of gene expression data

Bioinformatics ◽

10.1093/bioinformatics/btab035 ◽

2021 ◽

Author(s):

Ramon Viñas ◽

Helena Andrés-Terré ◽

Pietro Liò ◽

Kevin Bryson

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Synthetic Data ◽

Gene Clusters ◽

Supplementary Information ◽

Expression Data ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Wide Range ◽

Transcriptomics Data

Abstract Motivation High-throughput gene expression can be used to address a wide range of fundamental biological problems, but datasets of an appropriate size are often unavailable. Moreover, existing transcriptomics simulators have been criticized because they fail to emulate key properties of gene expression data. In this article, we develop a method based on a conditional generative adversarial network to generate realistic transcriptomics data for Escherichia coli and humans. We assess the performance of our approach across several tissues and cancer-types. Results We show that our model preserves several gene expression properties significantly better than widely used simulators, such as SynTReN or GeneNetWeaver. The synthetic data preserve tissue- and cancer-specific properties of transcriptomics data. Moreover, it exhibits real gene clusters and ontologies both at local and global scales, suggesting that the model learns to approximate the gene expression manifold in a biologically meaningful way. Availability and implementation Code is available at: https://github.com/rvinas/adversarial-gene-expression. Supplementary information Supplementary data are available at Bioinformatics online.

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CR-GAN: Learning Complete Representations for Multi-view Generation

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/131 ◽

2018 ◽

Cited By ~ 34

Author(s):

Yu Tian ◽

Xi Peng ◽

Long Zhao ◽

Shaoting Zhang ◽

Dimitris N. Metaxas

Keyword(s):

Supervised Learning ◽

State Of The Art ◽

Unlabeled Data ◽

Challenging Problem ◽

Generative Adversarial Network ◽

Single View ◽

Adversarial Network ◽

Complete Representations ◽

Wide Range ◽

And Robotics

Generating multi-view images from a single-view input is an important yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn ?incomplete? representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network.We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway to maintain the completeness of the learned embedding space. The two learning paths collaborate and compete in a parameter-sharing manner, yielding largely improved generality to ?unseen? dataset. More importantly, the two-pathway framework makes it possible to combine both labeled and unlabeled data for self-supervised learning, which further enriches the embedding space for realistic generations. We evaluate our approach on a wide range of datasets. The results prove that CR-GAN significantly outperforms state-of-the-art methods, especially when generating from ?unseen? inputs in wild conditions.

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Evaluation of conventional and deep learning based image harmonization methods in radiomics studies

Physics in Medicine and Biology ◽

10.1088/1361-6560/ac39e5 ◽

2021 ◽

Author(s):

Florent Tixier ◽

Vincent Jaouen ◽

Clément Hognon ◽

Olivier Gallinato ◽

Thierry Colin ◽

...

Keyword(s):

Prediction Models ◽

Texture Features ◽

Reference Image ◽

P Value ◽

Survival Prediction ◽

Shape Features ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Histogram Matching ◽

The Impact

Abstract Objective: To evaluate the impact of image harmonization on outcome prediction models using radiomics. Approach: 234 patients from the Brain Tumor Image Segmentation Benchmark (BRATS) dataset with T1 MRI were enrolled in this study. Images were harmonized through a reference image using histogram matching (HHM) and a generative adversarial network (GAN)-based method (HGAN). 88 radiomics features were extracted on HHM, HGAN and original (HNONE) images. Wilcoxon paired test was used to identify features significantly impacted by the harmonization protocol used. Radiomic prediction models were built using feature selection with the Least Absolute Shrinkage and Selection Operator (LASSO) and Kaplan-Meier analysis. Main results: More than 50% of the features (49/88) were statistically modified by the harmonization with HHM and 55 with HGAN (adjusted p-value < 0.05). The contribution of histogram and texture features selected by the LASSO, in comparison to shape features that were not impacted by harmonization, was higher in harmonized datasets (47% for Hnone, 62% for HHM and 71% for HGAN). Both image-based harmonization methods allowed to split patients into two groups with significantly different survival (p<0.05). With the HGAN images, we were also able to build and validate a model using only features impacted by the harmonization (median survivals of 189 vs. 437 days, p=0.006) Significance: Data harmonization in a multi-institutional cohort allows to recover the predictive value of some radiomics features that was lost due to differences in the image properties across centers. In terms of ability to build survival prediction models in the BRATS dataset, the loss of power from impacted histogram and heterogeneity features was compensated by the selection of additional shape features. The harmonization using a GAN-based approach outperformed the histogram matching technique, supporting the interest for the development of new advanced harmonization techniques for radiomic analysis purposes.

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Semi-Supervised Generative Adversarial Network for Sentiment Analysis of drug reviews

10.36227/techrxiv.17075054 ◽

2021 ◽

Author(s):

Cristóbal Colón-Ruiz

Keyword(s):

Sentiment Analysis ◽

Language Processing ◽

Automated Analysis ◽

Learning Approaches ◽

Training Set ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Wide Range ◽

Online User Reviews

<div>Sentiment analysis has become a very popular research topic and covers a wide range of domains such as economy, politics and health. In the pharmaceutical field, automated analysis of online user reviews provides information on the effectiveness and potential side effects of drugs, which could be used to improve pharmacovigilance systems. Deep learning approaches have revolutionized the field of Natural Language Processing (NLP), achieving state-of-the-art results in many tasks, such as sentiment analysis.</div><div>These methods require large annotated datasets to train their models. However, in most real-world scenarios, obtaining high-quality labeled datasets is an expensive and time-consuming task. In contrast, unlabeled texts task can be, generally, easily obtained. </div><div>In this work, we propose a semi-supervised approach based on a Semi-Supervised Generative Adversarial Network (SSGAN) to address the lack of labeled data for the sentiment analysis of drug reviews, and improve the results provided by supervised approaches in this task.</div><div>To evaluate the real contribution of this approach, we present a benchmark comparison between our semi-supervised approach and a supervised approach, which uses a similar architecture but without the generative adversal setting. </div><div>Experimental results show better performance of the semi-supervised approach when annotated reviews are less than ten percent of the training set, obtaining a significant improvement for the classification of neutral reviews, the class with least examples. To the best of our knowledge, this is the first study that applies a SSGAN to the sentiment classification of drug reviews. Our semi-supervised approach provides promising results for dealing with the shortage of annotated dataset, but there is still much room to improvement.</div>

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Semi-Supervised Generative Adversarial Network for Sentiment Analysis of drug reviews

10.36227/techrxiv.17075054.v1 ◽

2021 ◽

Author(s):

Cristóbal Colón-Ruiz

Keyword(s):

Sentiment Analysis ◽

Language Processing ◽

Automated Analysis ◽

Learning Approaches ◽

Training Set ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Wide Range ◽

Online User Reviews

<div>Sentiment analysis has become a very popular research topic and covers a wide range of domains such as economy, politics and health. In the pharmaceutical field, automated analysis of online user reviews provides information on the effectiveness and potential side effects of drugs, which could be used to improve pharmacovigilance systems. Deep learning approaches have revolutionized the field of Natural Language Processing (NLP), achieving state-of-the-art results in many tasks, such as sentiment analysis.</div><div>These methods require large annotated datasets to train their models. However, in most real-world scenarios, obtaining high-quality labeled datasets is an expensive and time-consuming task. In contrast, unlabeled texts task can be, generally, easily obtained. </div><div>In this work, we propose a semi-supervised approach based on a Semi-Supervised Generative Adversarial Network (SSGAN) to address the lack of labeled data for the sentiment analysis of drug reviews, and improve the results provided by supervised approaches in this task.</div><div>To evaluate the real contribution of this approach, we present a benchmark comparison between our semi-supervised approach and a supervised approach, which uses a similar architecture but without the generative adversal setting. </div><div>Experimental results show better performance of the semi-supervised approach when annotated reviews are less than ten percent of the training set, obtaining a significant improvement for the classification of neutral reviews, the class with least examples. To the best of our knowledge, this is the first study that applies a SSGAN to the sentiment classification of drug reviews. Our semi-supervised approach provides promising results for dealing with the shortage of annotated dataset, but there is still much room to improvement.</div>

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WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals

Sensors ◽

10.3390/s22010402 ◽

2022 ◽

Vol 22 (1) ◽

pp. 402

Author(s):

Zhanjun Hao ◽

Juan Niu ◽

Xiaochao Dang ◽

Zhiqiang Qiao

Keyword(s):

Action Recognition ◽

Recognition Performance ◽

Generative Adversarial Network ◽

Motion Recognition ◽

Recognition Model ◽

Adversarial Network ◽

Wide Range ◽

Wireless Signal ◽

The Difference ◽

Model Training

Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually carries a large amount of human-related information, the motion-recognition model trained for a specific person usually does not work well in predicting another person’s motion. To deal with the difference, we propose a personnel-independent action-recognition model called WiPg, which is built by convolutional neural network (CNN) and generative adversarial network (GAN). According to CSI data of 14 yoga movements of 10 experimenters with different body types, model training and testing were carried out, and the recognition results, independent of bod type, were obtained. The experimental results show that the average correct rate of WiPg can reach 92.7% for recognition of the 14 yoga poses, and WiPg realizes “cross-personnel” movement recognition with excellent recognition performance.

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