arXiv subjects

Guang Yang

Publications and source records attributed to Guang Yang.

At least 271 records · Page 15Linked to original sources

arXiv2024

MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation

The recent Mamba model has shown remarkable adaptability for visual representation learning, including in medical imaging tasks. This study introduces MambaMIR, a Mamba-based model for medical image reconstruction, as well as its Generative Adversarial Network-based variant, MambaMIR-GAN. Our proposed MambaMIR inherits several advantages, such as linear complexity, global receptive fields, and dynamic weights, from the original Mamba model. The innovated arbitrary-mask mechanism effectively adapt Mamba to our image reconstruction task, providing randomness for subsequent Monte Carlo-based uncertainty estimation. Experiments conducted on various medical image reconstruction tasks, including fast MRI and SVCT, which cover anatomical regions such as the knee, chest, and abdomen, have demonstrated that MambaMIR and MambaMIR-GAN achieve comparable or superior reconstruction results relative to state-of-the-art methods. Additionally, the estimated uncertainty maps offer further insights into the reliability of the reconstruction quality. The code is publicly available at https://github.com/ayanglab/MambaMIR.

eess.IV↗

arXiv2024

Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba

Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with global sensitivity. The emerging Mamba has shown superiority in learning visual representation, which combines the advantages of linear scalability and global sensitivity. In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation. A novel Arbitrary Scan Masking (ASM) mechanism "masks out" redundant information to introduce randomness for further uncertainty estimation. Compared to the commonly used Monte Carlo (MC) dropout, our proposed MC-ASM provides an uncertainty map without the need for hyperparameter tuning and mitigates the performance drop typically observed when applying dropout to low-level tasks. For further texture preservation and better perceptual quality, we employ the wavelet transformation into MambaMIR and explore its variant based on the Generative Adversarial Network, namely MambaMIR-GAN. Comprehensive experiments have been conducted for multiple representative medical image reconstruction tasks, demonstrating that the proposed MambaMIR and MambaMIR-GAN outperform other baseline and state-of-the-art methods in different reconstruction tasks, where MambaMIR achieves the best reconstruction fidelity and MambaMIR-GAN has the best perceptual quality. In addition, our MC-ASM provides uncertainty maps as an additional tool for clinicians, while mitigating the typical performance drop caused by the commonly used dropout.

eess.IV↗

arXiv2024

Low-rank based motion correction followed by automatic frame selection in DT-CMR

Motivation: Post-processing of in-vivo diffusion tensor CMR (DT-CMR) is challenging due to the low SNR and variation in contrast between frames which makes image registration difficult, and the need to manually reject frames corrupted by motion. Goals: To develop a semi-automatic post-processing pipeline for robust DT-CMR registration and automatic frame selection. Approach: We used low intrinsic rank averaged frames as the reference to register other low-ranked frames. A myocardium-guided frame selection rejected the frames with signal loss, through-plane motion and poor registration. Results: The proposed method outperformed our previous noise-robust rigid registration on helix angle data quality and reduced negative eigenvalues in healthy volunteers.

eess.IV↗

arXiv2024

CodeScore-R: An Automated Robustness Metric for Assessing the FunctionalCorrectness of Code Synthesis

Evaluation metrics are crucial in the field of code synthesis. Commonly used code evaluation metrics canbe classified into three types: match-based, semantic-based, and execution-based. Among them, the execution-basedPass@k metric accurately assesses the functionality of predicted code by executing test cases. However, calculatingthis metric requires a significant amount of overhead, necessitating the design of an automated evaluation metric thatcan assess the functionality of predicted code without the need for test cases. Additionally, a good evaluation metricshould be robust, that is the metric can maintain its accuracy even when the predicted code undergoes minor changes.To address these challenges, we propose an automated robust metric, called CodeScore-R, based on UniXcoder andcontrastive learning, for evaluating the functionality of code synthesis. CodeScore-R employs techniques such assketch-based processing, syntactic-equivalent transformations, and mutation testing to effectively mitigate theinterference caused by identifiers, syntax structures, and operators on evaluation results. Experimental resultsdemonstrate that in the tasks of code generation and migration in Java and Python, CodeScore-R outperforms otherevaluation metrics and is more closely aligned with the Pass@k metric, while exhibiting stronger robustness.

cs.SE↗

arXiv2024

An investigation of anisotropy in the bubbly turbulent flow via direct numerical simulations

This study explores the dynamics of dispersed bubbly turbulent flow in a channel using interface-resolved direct numerical simulation (DNS) with an efficient Coupled Level-Set Volume-of-Fluid (CLSVOF) solver. The influence of number of bubbles (96 and 192), flow direction, and Eotvos number was examined across eight distinct cases. The results indicate that in upward flows, bubbles tend to accumulate near the wall, with smaller Eotvos numbers bringing them closer to the wall and enhancing energy dissipation through increased turbulence and vorticity. This proximity causes the liquid phase velocity to attenuate, and the bubbles, being more spherical, induce more isotropic turbulence. Conversely, in downward flows, bubbles cluster in the middle of the channel and induce additional pseudo-turbulence in the channel center, which induce additional turbulent kinetic energy in the channel center. The study further examines budget of Turbulent Kinetic Energy (TKE) and the exact balance equation for the Reynolds stresses, revealing that near-wall bubble motion generates substantial velocity gradients, particularly in the wall-normal direction, significantly impacting the turbulence structure.

physics.flu-dyn↗

arXiv2024

Deep asymmetric mixture model for unsupervised cell segmentation

Automated cell segmentation has become increasingly crucial for disease diagnosis and drug discovery, as manual delineation is excessively laborious and subjective. To address this issue with limited manual annotation, researchers have developed semi/unsupervised segmentation approaches. Among these approaches, the Deep Gaussian mixture model plays a vital role due to its capacity to facilitate complex data distributions. However, these models assume that the data follows symmetric normal distributions, which is inapplicable for data that is asymmetrically distributed. These models also obstacles weak generalization capacity and are sensitive to outliers. To address these issues, this paper presents a novel asymmetric mixture model for unsupervised cell segmentation. This asymmetric mixture model is built by aggregating certain multivariate Gaussian mixture models with log-likelihood and self-supervised-based optimization functions. The proposed asymmetric mixture model outperforms (nearly 2-30% gain in dice coefficient, p<0.05) the existing state-of-the-art unsupervised models on cell segmentation including the segment anything.

cs.CV↗

arXiv2024

Ta2Pd3Te5 topological thermometer

In recent decades, there has been a persistent pursuit of applications for surface/edge states in topological systems, driven by their dissipationless transport effects. However, there have been limited tangible breakthroughs in this field. This work demonstrates the remarkable properties of the topological insulator Ta2Pd3Te5, as a thermometer. This material exhibits a power-law correlation in temperature-dependent resistance at low temperatures, stemming from its Luttinger liquid behavior of edge states, while exhibiting semiconductor behavior at high temperatures. The power-law behavior effectively addresses the issue of infinite resistance in semiconductor thermometers at ultra-low temperatures, thereby playing a crucial role in enabling efficient thermometry in refrigerators supporting millikelvin temperatures or below. By employing chemical doping, adjusting thickness, and controlling gate voltage, its power-law behavior and semiconductor behavior can be effectively modulated. This enables efficient thermometry spanning from millikelvin temperatures to room temperature, and allows for precise local temperature measurement. Furthermore, this thermometer exhibits excellent temperature sensitivity and resolution, and can be fine-tuned to show small magnetoresistance. In summary, the Ta2Pd3Te5 thermometer, also referred to as a topological thermometer, exhibits outstanding performance and significant potential for measuring a wider range of temperatures compared to conventional low-temperature thermometers.

cond-mat.mtrl-sci↗

arXiv2024

Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation

Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging. However, procuring appropriate training data for these Super-Resolution (SR) models is challenging. Previous SR research has simulated thick-slice CT images from thin-slice CT images to create training pairs. However, these methods either rely on simplistic interpolation techniques that lack realism or sinogram reconstruction, which require the release of raw data and complex reconstruction algorithms. Thus, we introduce a simple yet realistic method to generate thick CT images from thin-slice CT images, facilitating the creation of training pairs for SR algorithms. The training pairs produced by our method closely resemble real data distributions (PSNR=49.74 vs. 40.66, p$<$0.05). A multivariate Cox regression analysis involving thick slice CT images with lung fibrosis revealed that only the radiomics features extracted using our method demonstrated a significant correlation with mortality (HR=1.19 and HR=1.14, p$<$0.005). This paper represents the first to identify and address the challenge of generating appropriate paired training data for Deep Learning-based CT SR models, which enhances the efficacy and applicability of SR models in real-world scenarios.

eess.IV↗

arXiv2024

Decoding Decision Reasoning: A Counterfactual-Powered Model for Knowledge Discovery

In medical imaging, particularly in early disease detection and prognosis tasks, discerning the rationale behind an AI model's predictions is crucial for evaluating the reliability of its decisions. Conventional explanation methods face challenges in identifying discernible decisive features in medical image classifications, where discriminative features are subtle or not immediately apparent. To bridge this gap, we propose an explainable model that is equipped with both decision reasoning and feature identification capabilities. Our approach not only detects influential image patterns but also uncovers the decisive features that drive the model's final predictions. By implementing our method, we can efficiently identify and visualise class-specific features leveraged by the data-driven model, providing insights into the decision-making processes of deep learning models. We validated our model in the demanding realm of medical prognosis task, demonstrating its efficacy and potential in enhancing the reliability of AI in healthcare and in discovering new knowledge in diseases where prognostic understanding is limited.

cs.CV↗

arXiv2024

Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images

AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world. However, the issue of accurate identification of these synthetic images, particularly when they exhibit remarkable realism with their real copies, remains a concern. To mitigate this challenge, image generators such as DALLE and Imagen, have integrated digital watermarks aimed at facilitating the discernment of synthetic images' authenticity. These watermarks are embedded within the image pixels and are invisible to the human eye while remains their detectability. Nevertheless, a comprehensive investigation into the potential impact of these invisible watermarks on the utility of synthetic medical images has been lacking. In this study, we propose the incorporation of invisible watermarks into synthetic medical images and seek to evaluate their efficacy in the context of downstream classification tasks. Our goal is to pave the way for discussions on the viability of such watermarks in boosting the detectability of synthetic medical images, fortifying ethical standards, and safeguarding against data pollution and potential scams.

eess.IV↗

arXiv2024

Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration

Diffusion tensor cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure. Image registration is essential to correct image shifts due to intra and inter breath-hold motion and imperfect cardiac triggering. Registration is challenging in DT-CMR due to the low signal-to-noise and various contrasts induced by the diffusion encoding in the myocardium and surrounding organs. Traditional deformable registration corrects through-plane motion but at the risk of destroying the texture information while rigid registration inefficiently discards frames with local deformation. In this study, we explored the possibility of deep learning-based deformable registration on DT-CMR. Based on the noise suppression using low-rank features and diffusion encoding suppression using variational auto encoder-decoder, a B-spline based registration network extracted the displacement fields and maintained the texture features of DT-CMR. In this way, our method improved the efficiency of frame utilization, manual cropping, and computational speed.

eess.IV↗

arXiv2024

AI for Manufacturing and Healthcare: a chemistry and engineering perspective

Artificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment.

cond-mat.mtrl-sci↗

arXiv2024

A robust and tunable Luttinger liquid in correlated edge of transition-metal second-order topological insulator Ta$_2$Pd$_3$Te$_5$

The interplay between topology and interaction always plays an important role in condensed matter physics and induces many exotic quantum phases, while rare transition metal layered material (TMLM) has been proved to possess both. Here we report a TMLM Ta$_2$Pd$_3$Te$_5$ has the two-dimensional second-order topology (also a quadrupole topological insulator) with correlated edge states - Luttinger liquid. It is ascribed to the unconventional nature of the mismatch between charge- and atomic- centers induced by a remarkable double-band inversion. This one-dimensional protected edge state preserves the Luttinger liquid behavior with robustness and universality in scale from micro- to macro- size, leading to a significant anisotropic electrical transport through two-dimensional sides of bulk materials. Moreover, the bulk gap can be modulated by the thickness, resulting in an extensive-range phase diagram for Luttinger liquid. These provide an attractive model to study the interaction and quantum phases in correlated topological systems.

cond-mat.mes-hall↗

arXiv2024

4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs

Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing. This deficit stems, at least in part, from the lack of established/public RDB benchmarks as needed for training and evaluation purposes. As a result, related model development thus far often defaults to tabular approaches trained on ubiquitous single-table benchmarks, or on the relational side, graph-based alternatives such as GNNs applied to a completely different set of graph datasets devoid of tabular characteristics. To more precisely target RDBs lying at the nexus of these two complementary regimes, we explore a broad class of baseline models predicated on: (i) converting multi-table datasets into graphs using various strategies equipped with efficient subsampling, while preserving tabular characteristics; and (ii) trainable models with well-matched inductive biases that output predictions based on these input subgraphs. Then, to address the dearth of suitable public benchmarks and reduce siloed comparisons, we assemble a diverse collection of (i) large-scale RDB datasets and (ii) coincident predictive tasks. From a delivery standpoint, we operationalize the above four dimensions (4D) of exploration within a unified, scalable open-source toolbox called 4DBInfer. We conclude by presenting evaluations using 4DBInfer, the results of which highlight the importance of considering each such dimension in the design of RDB predictive models, as well as the limitations of more naive approaches such as simply joining adjacent tables. Our source code is released at https://github.com/awslabs/multi-table-benchmark .

cs.LG↗

arXiv2024

Realisation of de Gennes$'$ Absolute Superconducting Switch with a Heavy Metal Interface

In 1966, Pierre-Gilles de Gennes proposed a non-volatile mechanism for switching superconductivity on and off in a magnetic device. This involved a superconductor (S) sandwiched between ferromagnetic (F) insulators in which the net magnetic exchange field could be controlled through the magnetisation-orientation of the F layers. Because superconducting switches are attractive for a range of applications, extensive studies have been carried out on $F/S/F$ structures. Although these have demonstrated a sensitivity of the superconducting critical temperature ($T_{c}$) to parallel (P) and antiparallel (AP) magnetisation-orientations of the F layers, corresponding shifts in $T_c$ (i.e., $ΔT_c = T_{c,AP} - T_{c,P}$) are lower than predicted with $ΔT_c$ only a small fraction of $T_{c,AP}$, precluding the development of applications. Here, we report $EuS/Au/Nb/EuS$ structures where EuS is an insulating ferromagnet, Nb is a superconductor and Au is a heavy metal. For P magnetisations, the superconducting state in this structure is quenched down to the lowest measured temperature of 20 mK meaning that $ΔT_c/T_{c,AP}$ is practically 1. The key to this so-called absolute switching effect is a sizable spin-mixing conductance at the $EuS/Au$ interface which ensures a robust magnetic proximity effect, unlocking the potential of $F/S/F$ switches for low power electronics.

cond-mat.supr-con↗

arXiv2024

The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow imaging and motion artifacts. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI. CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techniques, physical modeling, and model complexity, thereby providing valuable insights for further developments in this field.

eess.IV↗

arXiv2024

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains prohibitively time-consuming. While significant efforts have been made towards enhancing airway modelling, current public-available datasets concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for prognosis, a strong airway-derived biomarker (Hazard ratio>1.5, p<0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

eess.IV↗

arXiv2024

Multi-dimensional Backward Stochastic Differential Equations of Diagonally Quadratic Generators with a Special Structure

The present paper is devoted to the well-posedness of a type of multi-dimensional backward stochastic differential equations (BSDEs) with a diagonally quadratic generator. We give a new priori estimate, and prove that the BSDE admits a unique solution on a given interval when the generator has a sufficiently small growth of the off-diagonal elements (i.e., for each $i$, the $i$-th component of the generator has a small growth of the $j$-th row $z^j$ of the variable $z$ for each $j \neq i$). Finally, we give a solvability result when the diagonally quadratic generator is triangular.

math.PR↗