arXiv subjects

Guang Yang

Publications and source records attributed to Guang Yang.

At least 379 records · Page 21Linked to original sources

arXiv2022

Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal

As diffusion tensor imaging (DTI) gains popularity in cardiac imaging due to its unique ability to non-invasively assess the cardiac microstructure, deep learning-based Artificial Intelligence is becoming a crucial tool in mitigating some of its drawbacks, such as the long scan times. As it often happens in fast-paced research environments, a lot of emphasis has been put on showing the capability of deep learning while often not enough time has been spent investigating what input and architectural properties would benefit cardiac DTI acceleration the most. In this work, we compare the effect of several input types (magnitude images vs complex images), multiple dimensionalities (2D vs 3D operations), and multiple input types (single slice vs multi-slice) on the performance of a model trained to remove artefacts caused by a simultaneous multi-slice (SMS) acquisition. Despite our initial intuition, our experiments show that, for a fixed number of parameters, simpler 2D real-valued models outperform their more advanced 3D or complex counterparts. The best performance is although obtained by a real-valued model trained using both the magnitude and phase components of the acquired data. We believe this behaviour to be due to real-valued models making better use of the lower number of parameters, and to 3D models not being able to exploit the spatial information because of the low SMS acceleration factor used in our experiments.

eess.IV↗

arXiv2022

Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey

Data quality is the key factor for the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can help improve the accuracy, robustness and privacy of downstream AI algorithms. However, access to good quality datasets is limited by the technical difficulty of data acquisition and large-scale sharing of healthcare data is hindered by strict ethical restrictions. Data synthesis algorithms, which generate data with a similar distribution as real clinical data, can serve as a potential solution to address the scarcity of good quality data during the development of trustworthy AI. However, state-of-the-art data synthesis algorithms, especially deep learning algorithms, focus more on imaging data while neglecting the synthesis of non-imaging healthcare data, including clinical measurements, medical signals and waveforms, and electronic healthcare records (EHRs). Thus, in this paper, we will review the synthesis algorithms, particularly for non-imaging medical data, with the aim of providing trustworthy AI in this domain. This tutorial-styled review paper will provide comprehensive descriptions of non-imaging medical data synthesis on aspects including algorithms, evaluations, limitations and future research directions.

cs.LG↗

arXiv2022

MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge, including five tracks focusing on novel image sensors and imaging algorithms. In this paper, Quad Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of Quad CFA to Bayer at full resolution, is introduced. The participants were provided a new dataset, including 70 (training) and 15 (validation) scenes of high-quality Quad and Bayer pairs. In addition, for each scene, Quad of different noise levels was provided at 0dB, 24dB, and 42dB. All the data were captured using a Quad sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics, including PSNR, SSIM, LPIPS, and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.

eess.IV↗

arXiv2022

MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGBW Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of RGBW CFA to Bayer at full resolution, is introduced. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pairs. In addition, for each scene, RGBW of different noise levels was provided at 0dB, 24dB, and 42dB. All the data were captured using an RGBW sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics including PSNR, SSIM, LPIPS, and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.

cs.CV↗

arXiv2022

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions. especially for cohorts with different lung diseases. Attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19 and pulmonary fibrosis.

eess.IV↗

arXiv2022

Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space

Human motion transfer refers to synthesizing photo-realistic and temporally coherent videos that enable one person to imitate the motion of others. However, current synthetic videos suffer from the temporal inconsistency in sequential frames that significantly degrades the video quality, yet is far from solved by existing methods in the pixel domain. Recently, some works on DeepFake detection try to distinguish the natural and synthetic images in the frequency domain because of the frequency insufficiency of image synthesizing methods. Nonetheless, there is no work to study the temporal inconsistency of synthetic videos from the aspects of the frequency-domain gap between natural and synthetic videos. In this paper, we propose to delve into the frequency space for temporally consistent human motion transfer. First of all, we make the first comprehensive analysis of natural and synthetic videos in the frequency domain to reveal the frequency gap in both the spatial dimension of individual frames and the temporal dimension of the video. To close the frequency gap between the natural and synthetic videos, we propose a novel Frequency-based human MOtion TRansfer framework, named FreMOTR, which can effectively mitigate the spatial artifacts and the temporal inconsistency of the synthesized videos. FreMOTR explores two novel frequency-based regularization modules: 1) the Frequency-domain Appearance Regularization (FAR) to improve the appearance of the person in individual frames and 2) Temporal Frequency Regularization (TFR) to guarantee the temporal consistency between adjacent frames. Finally, comprehensive experiments demonstrate that the FreMOTR not only yields superior performance in temporal consistency metrics but also improves the frame-level visual quality of synthetic videos. In particular, the temporal consistency metrics are improved by nearly 30% than the state-of-the-art model.

cs.CV↗

arXiv2022

A Long Time Ago in a Galaxy Far, Far Away: A Candidate z ~ 12 Galaxy in Early JWST CEERS Imaging

We report the discovery of a candidate galaxy with a photo-z of z~12 in the first epoch of the JWST Cosmic Evolution Early Release Science (CEERS) Survey. Following conservative selection criteria we identify a source with a robust z_phot = 11.8^+0.3_-0.2 (1-sigma uncertainty) with m_F200W=27.3, and >7-sigma detections in five filters. The source is not detected at lambda < 1.4um in deep imaging from both HST and JWST, and has faint ~3-sigma detections in JWST F150W and HST F160W, which signal a Ly-alpha break near the red edge of both filters, implying z~12. This object (Maisie's Galaxy) exhibits F115W-F200W > 1.9 mag (2-sigma lower limit) with a blue continuum slope, resulting in 99.6% of the photo-z PDF favoring z > 11. All data quality images show no artifacts at the candidate's position, and independent analyses consistently find a strong preference for z > 11. Its colors are inconsistent with Galactic stars, and it is resolved (r_h = 340 +/- 14 pc). Maisie's Galaxy has log M*/Msol ~ 8.5 and is highly star-forming (log sSFR ~ -8.2 yr^-1), with a blue rest-UV color (beta ~ -2.5) indicating little dust though not extremely low metallicity. While the presence of this source is in tension with most predictions, it agrees with empirical extrapolations assuming UV luminosity functions which smoothly decline with increasing redshift. Should followup spectroscopy validate this redshift, our Universe was already aglow with galaxies less than 400 Myr after the Big Bang.

astro-ph.GA↗

arXiv2022

Dark Sector Studies with Neutrino Beams

An array of powerful neutrino-beam experiments will study the fundamental properties of neutrinos with unprecedented precision in the coming years. Along with their primary neutrino-physics motivations, there has been growing recognition that these experiments can carry out a rich program of searches for new, light, weakly-coupled particles that are part of a dark sector. In this white paper, we review the diverse theoretical motivations for dark sectors and the capabilities of neutrino beam experiments to probe a wide range of models and signatures. We also examine the potential obstacles that could limit these prospects and identify concrete steps needed to realize an impactful dark sector search program in this and coming decades.

hep-ph↗

arXiv2022

CEERS Key Paper III: The Resolved Host Properties of AGN at 3 < z < 5 with JWST

We report on the host properties of five X-ray luminous Active Galactic Nuclei (AGN) identified at $3 < z < 5$ in the first epoch of imaging from the Cosmic Evolution Early Release Science Survey (CEERS). Each galaxy has been imaged with the \textit{James Webb Space Telescope} (\jwst) Near-Infrared Camera (NIRCam), which provides spatially resolved, rest-frame optical morphologies at these redshifts. We also derive stellar masses and star formation rates for each host galaxy by fitting its spectral energy distribution using a combination of galaxy and AGN templates. The AGN hosts have an average stellar mass of ${\rm log}(M_{*}/{\rm M_{\odot}} )= 11.0$, making them among the most massive galaxies detected at this redshift range in the current CEERS pointings, even after accounting for nuclear light from the AGN. We find that three of the AGN hosts have spheroidal morphologies, one is a bulge-dominated disk and one host is dominated by point-like emission. None are found to show strong morphological disturbances that might indicate a recent interaction or merger event. Notably, all four of the resolved hosts have rest-frame optical colors consistent with a quenched or post-starburst stellar population. The presence of AGN in passively evolving galaxies at $z>3$ is significant because a rapid feedback mechanism is required in most semi-analytic models and cosmological simulations to explain the growing population of massive quiescent galaxies observed at these redshifts. Our findings are in general agreement with this picture and show that AGN can continue to inject energy into these systems after their star formation is curtailed, possibly helping to maintain their quiescent state.

astro-ph.GA↗

arXiv2022

Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation

Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classification. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and robust segmentation results. We used the Theory of Evidence to interpret the output of a neural network as evidence values gathered from input features. Following Subjective Logic, evidence was parameterized as a Dirichlet distribution, and predicted probabilities were treated as subjective opinions. To evaluate the performance of our model on segmentation and uncertainty estimation, we conducted quantitative and qualitative experiments on the BraTS 2020 dataset. The results demonstrated the top performance of the proposed method in quantifying segmentation uncertainty and robustly segmenting tumors. Furthermore, our proposed new framework maintained the advantages of low computational cost and easy implementation and showed the potential for clinical application.

eess.IV↗

arXiv2022

Three-Sensor 3ω-2ω Method for the Simultaneous Measurement of Thermal Conductivity and Thermal Boundary Resistance in Film-on-Substrate Heterostructures

Solid heterostructures composed of substrates and epitaxial films are extensively used in advanced technologies, and their thermophysical properties fundamentally determine the performance, efficiency, reliability, and lifetime of the corresponding devices. However, an experimental method that is truly appropriate for the thermophysical property measurement of solid heterostructures is still lacking. To this end, a three-sensor 3ω-2ω method is proposed, which can simultaneously measure the thermal conductivities of the film and the substrate, along with the film-substrate thermal boundary resistance (TBR) in a single solid heterostructure without any reference samples, showing broad applicability for miscellaneous heterostructures with film thickness ranging from 100 nm to 10 μm. In this method, three parallel metal sensors with unequal width and spacing are fabricated on the sample surface, in which the two outer sensors are used as heaters, and the middle sensor is used as a detector. The respective 3ω signals of the two heaters and 2ω signal of the detector are measured, and then the thermophysical properties of the sample are fitted within 3D finite element simulations. To verify this method, two typical wide bandgap semiconductor heterojunctions, i.e., GaN on SiC (#SiC) and GaN on Si (#Si) with ~2.3 μm GaN epilayers, are measured. The thermal conductivity of the GaN film, the thermal conductivities of the SiC and Si substrates, and the GaN/substrate TBRs are derived, exhibiting good agreement with the reported values in the literature. The proposed method will provide a comprehensive solution for the thermophysical property measurements of various solid heterostructures.

physics.ins-det↗

arXiv2022

A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans

Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation. In COVID-19 computed tomography (CT) images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis. Based on this situation, some researchers propose the relevant DL models which can replace professional diagnostic specialists in clinics when lacking expertise. However, although DL methods have a stunning performance in medical image processing, the limited datasets can be a challenge in developing the accuracy of diagnosis at the human level. In addition, deep learning algorithms face the challenge of classifying and segmenting medical images in three or even multiple dimensions and maintaining high accuracy rates. Consequently, with a guaranteed high level of accuracy, our model can classify the patients' CT images into three types: Normal, Pneumonia and COVID. Subsequently, two datasets are used for segmentation, one of the datasets even has only a limited amount of data (20 cases). Our system combined the classification model and the segmentation model together, a fully integrated diagnostic model was built on the basis of ResNet50 and 3D U-Net algorithm. By feeding with different datasets, the COVID image segmentation of the infected area will be carried out according to classification results. Our model achieves 94.52% accuracy in the classification of lung lesions by 3 types: COVID, Pneumonia and Normal. For future medical use, embedding the model into the medical facilities might be an efficient way of assisting or substituting doctors with diagnoses, therefore, a broader range of the problem of variant viruses in the COVID-19 situation may also be successfully solved.

eess.IV↗

arXiv2022

Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network

Tissue segmentation is the mainstay of pathological examination, whereas the manual delineation is unduly burdensome. To assist this time-consuming and subjective manual step, researchers have devised methods to automatically segment structures in pathological images. Recently, automated machine and deep learning based methods dominate tissue segmentation research studies. However, most machine and deep learning based approaches are supervised and developed using a large number of training samples, in which the pixelwise annotations are expensive and sometimes can be impossible to obtain. This paper introduces a novel unsupervised learning paradigm by integrating an end-to-end deep mixture model with a constrained indicator to acquire accurate semantic tissue segmentation. This constraint aims to centralise the components of deep mixture models during the calculation of the optimisation function. In so doing, the redundant or empty class issues, which are common in current unsupervised learning methods, can be greatly reduced. By validation on both public and in-house datasets, the proposed deep constrained Gaussian network achieves significantly (Wilcoxon signed-rank test) better performance (with the average Dice scores of 0.737 and 0.735, respectively) on tissue segmentation with improved stability and robustness, compared to other existing unsupervised segmentation approaches. Furthermore, the proposed method presents a similar performance (p-value > 0.05) compared to the fully supervised U-Net.

eess.IV↗

arXiv2022

On Non-degenerate Chaos Processes

We consider a process $\{X_t\}_{0\leq t\leq 1}$ in a fixed Wiener chaos $\mathcal{H}_n$. We establish some non-degenerate properties and related results for $\{X_t\}_{0\leq t\leq 1}$. As an application, we show that solution to SDE driven by $\{X_t\}_{0\leq t\leq 1}$ admits a density. Our approach relies on an interplay between Malliavin calculus and analysis on Wiener space.

math.PR↗

arXiv2022

Large-Kernel Attention for 3D Medical Image Segmentation

Automatic segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of convolution and self-attention are combined in the proposed LK attention module, including local contextual information, long-range dependence, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into FCNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance. The performance improvement due to the proposed LK attention module was also statistically validated.

eess.IV↗

arXiv2022

Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges

Artificial intelligence has become pervasive across disciplines and fields, and biomedical image and signal processing is no exception. The growing and widespread interest on the topic has triggered a vast research activity that is reflected in an exponential research effort. Through study of massive and diverse biomedical data, machine and deep learning models have revolutionized various tasks such as modeling, segmentation, registration, classification and synthesis, outperforming traditional techniques. However, the difficulty in translating the results into biologically/clinically interpretable information is preventing their full exploitation in the field. Explainable AI (XAI) attempts to fill this translational gap by providing means to make the models interpretable and providing explanations. Different solutions have been proposed so far and are gaining increasing interest from the community. This paper aims at providing an overview on XAI in biomedical data processing and points to an upcoming Special Issue on Deep Learning in Biomedical Image and Signal Processing of the IEEE Signal Processing Magazine that is going to appear in March 2022.

cs.LG↗

arXiv2022

Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI

Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently. Meanwhile, novel deep learning paradigms, e.g., Transformer based models, are fast-growing in natural language processing and promptly developed for computer vision and medical image analysis due to their prominent performance. Nevertheless, due to the complexity of the Transformer, the application of fast MRI may not be straightforward. The main obstacle is the computational cost of the self-attention layer, which is the core part of the Transformer, can be expensive for high resolution MRI inputs. In this study, we propose a new Transformer architecture for solving fast MRI that coupled Shifted Windows Transformer with U-Net to reduce the network complexity. We incorporate deformable attention to construe the explainability of our reconstruction model. We empirically demonstrate that our method achieves consistently superior performance on the fast MRI task. Besides, compared to state-of-the-art Transformer models, our method has fewer network parameters while revealing explainability. The code is publicly available at https://github.com/ayanglab/SDAUT.

cs.CV↗

arXiv2022

Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels

The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need for rapid identification of infected patients and specific delineation of infection areas in computed tomography (CT) images. Although deep supervised learning methods have been established quickly, the scarcity of both image-level and pixel-level labels as well as the lack of explainable transparency still hinder the applicability of AI. Can we identify infected patients and delineate the infections with extreme minimal supervision? Semi-supervised learning has demonstrated promising performance under limited labelled data and sufficient unlabelled data. Inspired by semi-supervised learning, we propose a model-agnostic calibrated pseudo-labelling strategy and apply it under a consistency regularization framework to generate explainable identification and delineation results. We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data. Extensive experiments have shown that our model can efficiently utilize limited labelled data and provide explainable classification and segmentation results for decision-making in clinical routine. The code is available at https://github.com/ayanglab/XAI COVID-19.

eess.IV↗