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Modeling and Synthesis of Breast Cancer Optical Property Signatures With Generative Models

IEEE TRANSACTIONS ON MEDICAL IMAGING 2021年第6期40卷 1687-1701页

作者： Pardo, Arturo Streeter, Samuel S. Maloney, Benjamin W. Gutierrez-Gutierrez, Jose A. McClatchy, David M. Wells, Wendy A. Paulsen, Keith D. Lopez-Higuera, Jose M. Pogue, Brian W. Conde, Olga M. Univ Cantabria Photon Engn Grp Santander 39006 Spain Inst Invest Sanitaria Valdecilla IDIVAL Santander 39011 Spain Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Massachusetts Gen Hosp Radiat Drug Treatment Design Lab Boston MA 02114 USA Dartmouth Coll Geisel Sch Med Hanover NH 03755 USA Ctr Invest Biomed Red Bioingn Biomat & Nanomed CI Zaragoza 50018 Spain

Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.

关键词： Optical imaging Nonlinear optics Biomedical optical imaging Imaging Adaptive optics Pathology Optical scattering Biomedical optical imaging breast cancer tissue optical properties modeling pathology deep learning dimensionality reduction variational autoencoder convolutional neural networks

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Generating Responses With a Given Syntactic Pattern in Chinese Dialogues

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 2888-2898页

作者： Zhou, Yi Zheng, Xiaoqing Huang, Xuanjing Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

Recently, many efforts have been devoted to generating responses expressing a specific emotion or relating to a given topic in a controlled manner. However, limited attention has been given to generating responses with a specified syntactic pattern, which makes it possible to imitate someone's way of speaking in dialogue. To fulfill this goal, we propose two models to generate syntax-aware responses: a gross-constraint and a specific-constraint model. The former controls the syntactic patterns of generated responses at sentence-level, while the latter works at smaller language units, such as words or phrases, being capable of manipulating the syntactic structures of responses in a more subtle manner. The extensive experimental results on two different datasets show that both the two models not only can generate meaningful responses with a specific and coherent structure but also improve on the diversity of generated responses, with similar gains in readability, relevance, and diversity as measured by human judges.

关键词： Syntactics Process control Speech processing Computer architecture Chatbots Training Task analysis Controlled text generation stylized dialogue response generation syntactic patterns variational autoencoder deep neural networks

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Passive Sonar Target Classification Using Deep Generative β-VAE

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 808-812页

作者： Chandran, Satheesh C. Kamal, Suraj Mujeeb, A. Supriya, M. H. Cochin Univ Sci & Technol Dept Elect Kochi 682022 India Cochin Univ Sci & Technol Int Sch Photon Kochi 682022 India

The intrinsic complexity associated with passive sonar data makes the task of target recognition extremely challenging. The conventional classifier architectures based on hand-engineered feature transforms often fail miserably to disentangle the high-dimensional non-linear structures in the observed target records. Although the modern deep learning algorithms through hierarchical feature learning yield acceptable success rates, they often require tremendous amounts of data when trained in a supervised manner. An unsupervised generative framework utilizing a variational autoencoder (VAE) is proposed in this work in order to create better disentangled representations for the downstream classification task. The disentanglement is further enforced using a hyperparameter beta. For the purpose of better segregating the spectro-temporal features, an intermediate non-linearly scaled time-frequency representation is also employed in conjunction with beta-VAE. Experimental analysis of various classifier configurations yields encouraging results in terms of data efficiency and classification accuracy on target records collected from various locations of the Indian Ocean.

关键词： Task analysis Target recognition Acoustics Signal processing algorithms Oceans Machine learning algorithms Decoding Passive sonar target recognition deep learning variational autoencoder

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Adaptive map matching based on dynamic word embeddings for indoor positioning

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NEUROCOMPUTING 2023年第1期553卷

作者： Lan, Xinyue Zhang, Lijia Xiao, Zhuoling Yan, Bo Univ Elect Sci & Technol China Sch Informat & Commun Engn 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China UESTC Sch Informat & Commun Engn Chengdu Peoples R China

Map matching has been widely used in various indoor localization technologies. However, conventional map matching technologies based on probabilistic models, such as particle filter (PF), still have a series of limitations, such as underutilization of map information, poor generalization, and relatively low precision. To improve the performance of PF-based map matching technique, this paper proposes MapDem, a novel map matching model fusing dynamic word embeddings and variational autoencoder (VAE) to improve matching performance significantly. The key to our approach is to extract map information using dynamic word embeddings to represent each reachable point on the map as word vectors with allowable oriented trajectory information. The same point has different representations on different trajectories so that MapDem can adaptively learn the contextual information of map for position estimation. Unlike traditional particle filters, MapDem focuses on the learning of particle sets distribution by a statistical model, variational autoencoder (VAE), followed by estimating position with combined current and previous sequence information. Extensive experiments have been conducted with 610 trajectories in three real-world scenarios. Numerical results demonstrate the adaptability of MapDem which works equally well in all three different scenarios, outperforming traditional particle filters by 18% on average.

关键词： Map matching Dynamic word embeddings Particle filter variational autoencoder

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PFEMed: Few-shot medical image classification using prior guided feature enhancement

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PATTERN RECOGNITION 2023年 134卷

作者： Dai, Zhiyong Yi, Jianjun Yan, Lei Xu, Qingwen Hu, Liang Zhang, Qi Li, Jiahui Wang, Guoqiang DeepBlue Acad Sci Shanghai 200240 Peoples R China East China Univ Sci & Technol Sch Mech & Power Engn Shanghai 200237 Peoples R China Wenyao Technol Hainan 571924 Peoples R China ShanghaiTech Univ Sch Informat Sci Technol Shanghai 201210 Peoples R China Tongji Univ Shanghai 201804 Peoples R China Univ Technol Sydney Sydney Australia Univ Shanghai Sci & Technol Shanghai Peoples R China Shanghai Univ Engn Sci Sch Math Phys & Stat Shanghai 201620 Peoples R China

Deep learning-based methods have recently demonstrated outstanding performance on general image classification tasks. As optimization of these methods is dependent on a large amount of labeled data, their application in medical image classification is limited. To address this issue, we propose PFEMed, a novel few-shot classification method for medical images. To extract general and specific features from medical images, this method employs a dual-encoder structure, that is, one encoder with fixed weights pre-trained on public image classification datasets and another encoder trained on the target medical dataset. In addition, we introduce a novel prior-guided variational autoencoder (VAE) module to enhance the robustness of the target feature, which is the concatenation of the general and specific features. Then, we match the target features extracted from both the support and query medical image samples and pre-dict the category attribution of the query examples. Extensive experiments on several publicly available medical image datasets show that our method outperforms current state-of-the-art few-shot methods by a wide margin, particularly outperforming MetaMed on the Pap smear dataset by over 2.63%.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Deep learning Domain adaption Few-shot learning Medical image classification variational autoencoder

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Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第4期4卷 045028页

作者： Jadhav, Shreejit Shrivastava, Mihir Mitra, Sanjit Interuniv Ctr Astron & Astrophys IUCAA Post Bag 4 Pune 411007 India Swinburne Univ Technol Ctr Astrophys & Supercomp Hawthorn Vic 3122 Australia ARC Ctr Excellence Gravitat Wave Discovery OzGrav Melbourne Australia Indian Inst Technol IIT Kharagpur India

The ability of deep learning (DL) approaches to learn generalised signal and noise models, coupled with their fast inference on GPUs, holds great promise for enhancing gravitational-wave (GW) searches in terms of speed, parameter space coverage, and search sensitivity. However, the opaque nature of DL models severely harms their reliability. In this work, we meticulously develop a DL model stage-wise and work towards improving its robustness and reliability. First, we address the problems in maintaining the purity of training data by deriving a new metric that better reflects the visual strength of the 'chirp' signal features in the data. Using a reduced, smooth representation obtained through a variational auto-encoder (VAE), we build a classifier to search for compact binary coalescence (CBC) signals. Our tests on real LIGO data show an impressive performance of the model. However, upon probing the robustness of the model through adversarial attacks, its simple failure modes were identified, underlining how such models can still be highly fragile. As a first step towards bringing robustness, we retrain the model in a novel framework involving a generative adversarial network (GAN). Over the course of training, the model learns to eliminate the primary modes of failure identified by the adversaries. Although absolute robustness is practically impossible to achieve, we demonstrate some fundamental improvements earned through such training, like sparseness and reduced degeneracy in the extracted features at different layers inside the model. We show that these gains are achieved at practically zero loss in terms of model performance on real LIGO data before and after GAN training. Through a direct search on similar to 8.8 days of LIGO data, we recover two significant CBC events from GWTC-2.1 (Abbott et al 2022 2108.01045 [gr-qc]), GW190519_153544 and GW190521_074359. We also report the search sensitivity obtained from an injection study.

关键词： gravitational wave deep learning cbc search variational autoencoder generative adversarial network robust and reliable model

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Prediction of microstructure evolution at the atomic scale by deep generative model in combination with recurrent neural networks

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ACTA MATERIALIA 2023年第1期259卷

作者： Sase, Kohei Shibuta, Yasushi Univ Tokyo Dept Mat Engn 7-3-1 HongoBunkyo Ku Tokyo 1138656 Japan

A novel method to predict multi-atom cooperative phenomena at atomic scale is proposed based on a deep generative model in combination with recurrent neural network. The variational autoencoder (VAE) model successfully identifies three different crystal orientations in the polycrystalline nickel by using 10-dimensional latent variables and restores the image of atomic configurations reflecting the crystal orientation of each grain. Moreover, microstructure evolution of the polycrystalline iron is successfully predicted through three steps: dimensionality reduction of atomic coordinates from the MD simulation using the encoder, time evolution of latent variables using the long short-term memory (LSTM) model, and data restoration using the decoder. We successfully predict the microstructure that cannot be reproduced on the time scale of MD simulations by decoding latent variables in the future time from the LSTM model. This is a new attempt of acceleration of the MD simulation that differs significantly from conventional acceleration methods.

关键词： Molecular dynamics Microstructure Deep generative model Recurrent neural networks variational autoencoder Long short-term memory

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Highly Productive 3D Printing Process to Transcend Intractability in Materials and Geometries via Interactive Machine-Learning-Based Technique

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2023年第7期5卷

作者： Kim, Yuseok Park, Suk Hee Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea

Herein, a highly productive and defect-free 3D-printing system enforced by deep-learning (DL)-based anomaly detection and reinforcement-learning (RL)-based optimization processes is developed. Unpredictable defect factors, such as machine setting errors or unexpected material flow, are analyzed by image-based anomaly detection implemented using a variational autoencoder DL model. Real-time detection and in situ correction of defects are implemented by an autocalibration algorithm in conjunction with the DL system. In view of productivity enhancement, the optimized set of diversified printing speeds can be generated from virtual simulation of RL, which is established using a physics-based engineering model. The RL-simulated parameter set maximizes printing speed and minimizes deflection-related failures throughout the 3D-printing process. With the synergistic assistance of DL and RL techniques, the developed system can overcome the inherent challenging intractability of 3D printing in terms of material property and geometry, achieving high process productivity.

关键词： deep learning defect control reinforcement learning variational autoencoder 3D printing

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Synthesis of Synthetic Hyperspectral Images with Controllable Spectral Variability Using a Generative Adversarial Network

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REMOTE SENSING 2023年第16期15卷 3919-3919页

作者： Palsson, Burkni Ulfarsson, Magnus O. Sveinsson, Johannes R. Univ Iceland Fac Elect & Comp Engn IS-105 Reykjavik Iceland

In hyperspectral unmixing (HU), spectral variability in hyperspectral images (HSIs) is a major challenge which has received a lot of attention over the last few years. Here, we propose a method utilizing a generative adversarial network (GAN) for creating synthetic HSIs having a controllable degree of realistic spectral variability from existing HSIs with established ground truth abundance maps. Such synthetic images can be a valuable tool when developing HU methods that can deal with spectral variability. We use a variational autoencoder (VAE) to investigate how the variability in the synthesized images differs from the original images and perform blind unmixing experiments on the generated images to illustrate the effect of increasing the variability.

关键词： hyperspectral unmixing synthetic hyperspectral images generative adversarial network variational autoencoder deep learning neural network

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Separating group- and individual-level brain signatures in the newborn functional connectome: A deep learning approach

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NEUROIMAGE 2024年 299卷 120806-120806页

作者： Kim, Jung-Hoon De Asis-Cruz, Josepheen Limperopoulos, Catherine Childrens Natl Developing Brain Inst 111 Michigan Ave NW Washington DC 20010 USA

Recent studies indicate that differences in cognition among individuals may be partially attributed to unique brain wiring patterns. While functional connectivity (FC)-based fingerprinting has demonstrated high accuracy in identifying adults, early studies on neonates suggest that individualized FC signatures are absent. We posit that individual uniqueness is present in neonatal FC data and that conventional linear models fail to capture the rapid developmental trajectories characteristic of newborn brains. To explore this hypothesis, we employed a deep generative model, known as a variational autoencoder (VAE), leveraging two extensive public datasets: one comprising resting-state functional MRI (rs-fMRI) scans from 100 adults and the other from 464 neonates. VAE models trained on rs-fMRI from both adults and newborns produced superior age prediction performance (with r between predicted- and actual age similar to 0.7) and individual identification accuracy (similar to 45 %) compared to models trained solely on adult or neonatal data. The VAE model also showed significantly higher individual identification accuracy than linear models (=10 similar to 30 %). Importantly, the VAE differentiated connections reflecting age-related changes from those indicative of individual uniqueness, a distinction not possible with linear models. Moreover, we derived 20 latent variables, each corresponding to distinct patterns of cortical functional network (CFNs). These CFNs varied in their representation of brain maturation and individual signatures;notably, certain CFNs that failed to capture neurodevelopmental traits, in fact, exhibited individual signatures. CFNs associated with neonatal neurodevelopment predominantly encompassed unimodal regions such as visual and sensorimotor areas, whereas those linked to individual uniqueness spanned multimodal and transmodal brain regions. The VAE's capacity to extract features from rs-fMRI data beyond the capabilities of linear models posit

关键词： Neonate Resting-state fMRI variational autoencoder Subject identification Age prediction Individual uniqueness

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