arXiv subjects

Ni Yao

Publications and source records attributed to Ni Yao.

At least 19 recordsLinked to original sources

arXiv2026

MVMGNN;Multi-View Masked Graph Neural Network for Alzheimer's Disease Diagnosis using Structural MRI

Alzheimer's disease (AD) is a common neurodegenerative disorder, and early diagnosis is of great significance for delaying disease progression and enabling timely intervention. Mild cognitive impairment (MCI), which represents an intermediate clinical stage between cognitively normal aging and AD. Structural magnetic resonance imaging (sMRI) provides detailed characterization of anatomical structures and plays an important role in AD-related brain analysis. However, existing sMRI-based brain network methods typically rely on a single graph construction strategy, limiting their ability to jointly capture spatial relationships and morphological similarities between brain regions. To address these issues, this paper proposes an sMRI-based multi-view masked graph neural network model (MVMGNN) for AD diagnosis. A joint node-edge masking mechanism is proposed to simultaneously select radiomics feature dimensions and structural connections, reducing redundancy during graph learning. Furthermore, a patient-level cross-view gated fusion mechanism is proposed to integrate multi-view representations. Experimental results on the ADNI dataset demonstrate that MVMGNN outperforms several competing approaches in AD classification. Interpretability analysis further demonstrates that MVMGNN is able to identify key brain regions associated with AD, providing useful insights into discriminative patterns in sMRI-based brain networks.Our implementation is publicly available at https://github.com/chenzhao2023/MVMGNN_AD

cs.CV↗

arXiv2026

Point Cloud Registration for Fusion between SPECT MPI and CTA Images

Clinical fusion of Single Photon Emission Computed Tomography Myocardial Perfusion Imaging (SPECT MPI) and Computed Tomography Angiography (CTA) remains limited by cross-modality misregistration and reliance on manual landmarks, which can hinder accurate ischemia localization and lesion-level functional assessment. To address this issue, we propose a registration and fusion framework for SPECT MPI and CTA that integrates functional and structural information for comprehensive cardiac evaluation. The proposed pipeline performs U-Net-based segmentation on both modalities. On SPECT MPI, only the left ventricle (LV) is extracted, and anatomical landmarks are automatically derived from characteristic LV structures. On CTA, both ventricles are segmented, and their spatial relationship is used to automatically define landmarks at the interventricular septal junction. Scale-space consistency preprocessing and landmark-driven coarse registration are applied to mitigate initial misalignment. Based on this initialization, multiple fine registration methods are evaluated on LV epicardial surface point clouds, including ICP, SICP, CPD, CluReg, FFD, and BCPD-plus-plus. The resulting transformations are then propagated to voxel-level resampling for high-precision SPECT-CTA fusion. In a retrospective cohort of 60 patients, the proposed framework preserved sub-millimeter coronary detail from CTA while accurately overlaying quantitative SPECT perfusion. Among the evaluated methods, BCPD-plus-plus achieved the highest accuracy with a mean point cloud distance of 1.7 mm. By combining robust initialization, comparative fine registration, and voxel-level fusion, the proposed approach provides a practical solution for myocardial ischemia localization and functional evaluation of coronary lesions, while remaining independent of any specific fine registration algorithm.

cs.CV↗

arXiv2025

UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion

Background: Brain tumor segmentation has a significant impact on the diagnosis and treatment of brain tumors. Accurate brain tumor segmentation remains challenging due to their irregular shapes, vague boundaries, and high variability. Objective: We propose a brain tumor segmentation method that combines deep learning with prior knowledge derived from a region-growing algorithm. Methods: The proposed method utilizes a multi-scale feature fusion (MSFF) module and adaptive attention mechanisms (AAM) to extract multi-scale features and capture global contextual information. To enhance the model's robustness in low-confidence regions, the Monte Carlo Dropout (MC Dropout) strategy is employed for uncertainty estimation. Results: Extensive experiments demonstrate that the proposed method achieves superior performance on Brain Tumor Segmentation (BraTS) datasets, significantly outperforming various state-of-the-art methods. On the BraTS2021 dataset, the test Dice scores are 89.18% for Enhancing Tumor (ET) segmentation, 93.67% for Whole Tumor (WT) segmentation, and 91.23% for Tumor Core (TC) segmentation. On the BraTS2019 validation set, the validation Dice scores are 87.43%, 90.92%, and 90.40% for ET, WT, and TC segmentation, respectively. Ablation studies further confirmed the contribution of each module to segmentation accuracy, indicating that each component played a vital role in overall performance improvement. Conclusion: This study proposed a novel 3D brain tumor segmentation network based on the U-Net architecture. By incorporating the prior knowledge and employing the uncertainty estimation method, the robustness and performance were improved. The code for the proposed method is available at https://github.com/chenzhao2023/UPMAD_Net_BrainSeg.

cs.CV↗

arXiv2025

Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, requiring accurate segmentation and stenosis detection using Coronary Computed Tomography angiography (CCTA). Existing methods struggle with challenges such as low contrast, morphological variability and small vessel segmentation. To address these limitations, we propose the Myocardial Region-guided Feature Aggregation Net, a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via myocardial contour expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Encoding Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features. Additionally, Monte Carlo dropout f quantifies prediction uncertainty, supporting clinical interpretability. For stenosis detection, a morphology-based centerline extraction algorithm separates the vascular tree into anatomical branches, enabling cross-sectional area quantification and stenosis grading. The superiority of MGFA-Net was demonstrated by achieving an Dice score of 85.04%, an accuracy of 84.24%, an HD95 of 6.1294 mm, and an improvement of 5.46% in true positive rate for stenosis detection compared to3D U-Net. The integrated segmentation-to-stenosis pipeline provides automated, clinically interpretable CAD assessment, bridging deep learning with anatomical prior knowledge for precision medicine. Our code is publicly available at http://github.com/chenzhao2023/MGFA_CCTA

cs.CV↗

arXiv2025

Efficient transverse multi-wave interactions up to six-wave mixing in a high-Q lithium niobate microresonator

High-order nonlinear optical processes beyond four-wave mixing serve as fundamental tools for advancing photonic technologies, yet their practical implementation remains challenging due to stringent phase-matching requirements and inherently weak high-order nonlinear susceptibilities - limitations that persist even in state-of-the-art high-Q microresonators. In this work, we demonstrate a breakthrough in synthesizing transverse nonlinear processes up to six-wave mixing in an integrated lithium niobate microresonator, under single continuous-wave (CW) telecom-band laser pump. Our approach leverages self-organized subwavelength photorefractive gratings (SPGs) generated through bidirectional stimulated Raman scattering (SRS) process in the microresonator, without using two external counterpropagating lasers. Under 1546 nm pumping, bidirectional SRS at 1713 nm creates two counterpropagating light waves that spontaneously form SPGs. These SPGs critically enable broadband phase-matching compensation across 500 nm spectral range by providing additional momentum matching for transverse nonlinear processes while maintaining ultrahigh-Q factor. Moreover, cascaded SRS process is simultaneously activated to generate light signal for subsequent nonlinear interactions. This novel approach enables, to our knowledge, the first demonstration of single-pump phase-matched transverse sum-frequency generation (SFG) with record conversion efficiency (590%/W). Furthermore, transverse multi-wave mixing processes from four-wave to six-wave mixing processes are achieved with high conversion efficiencies for the first time using only the single CW pump, representing a notable advance in nonlinear integration.

physics.optics↗

arXiv2024

Polarization-entangled photon pairs generation from a single lithium niobate waveguide with single poling period

Polarization-entangled photon pairs are essential sources for photonic quantum information processing. However, generating entangled photon pairs with large detuning via spontaneous parametric down-conversion (SPDC) often requires complex configurations to compensate for phase matching. Here, we propose a simple and efficient scheme to generate polarization-entangled photon pairs based on type-0 SPDC in a thin-film lithium niobate waveguide with a single poling period. By utilizing the strong dispersion engineering capabilities of thin-film waveguides, we can achieve both degenerate and highly detuned entangled photon pairs. Furthermore, we demonstrate on-chip temporal compensation using an integrated waveguide structure. Our approach offers a compact and scalable solution for integrated quantum photonic circuits.

physics.optics↗

arXiv2023

Integrated multi-color Raman microlasers with ultra-low pump levels in single high-Q lithium niobate microdisks

Photonic integrated Raman microlasers, particularly discrete multi-color lasers which are crucial for extending the emission wavelength range of chip-scale laser sources to much shorter wavelength, are highly in demand for various spectroscopy, microscopy analysis, and biological detection. However, integrated multi-color Raman microlasers have yet to be demonstrated because of the requirement of high-Q microresonators possessing large second-order nonlinearity and strong Raman phonon branches and the challenging in cavity-enhanced multi-photon hyper-Raman scattering parametric process. In this work, integrated multi-color Raman lasers have been demonstrated for the first time at weak pump levels, via the excitation of high-Q (>6 X 10^6) phase-matched modes in single thin-film lithium niobate (TFLN) microresonators by dispersion engineering. Raman lasing was observed at 1712 nm for a 1546-nm pump threshold power of only 620 uW. Furthermore, multi-color Raman lasers were realized at discrete wavelengths of 1712 nm, 813 nm, 533 nm and 406 nm with pump levels as low as 1.60 mW, which is more than two order of magnitude lower than the current records (i.e., 200 mW) in bulk resonators, allowed by the fulfillment of the requisite conditions consisting of broadband natural phase match, multiple-resonance and high Q-factors.

physics.optics↗

arXiv2023

Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection

Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, current EAT segmentation methods do not consider positional information. Additionally, the detection of COVID-19 severity lacks consideration for EAT radiomics features, which limits interpretability. This study investigates the use of radiomics features from EAT and lungs to detect the severity of COVID-19 infections. A retrospective analysis of 515 patients with COVID-19 (Cohort1: 415, Cohort2: 100) was conducted using a proposed three-stage deep learning approach for EAT extraction. Lung segmentation was achieved using a published method. A hybrid model for detecting the severity of COVID-19 was built in a derivation cohort, and its performance and uncertainty were evaluated in internal (125, Cohort1) and external (100, Cohort2) validation cohorts. For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (+-0.011) and 0.968 (+-0.005), respectively. For severity detection, the hybrid model with radiomics features of both lungs and EAT showed improvements in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) compared to the model with only lung radiomics features. The hybrid model exhibited an increase of 0.1 (p<0.001), 19.3%, and 18.0% respectively, in the internal validation cohort and an increase of 0.09 (p<0.001), 18.0%, and 18.0%, respectively, in the external validation cohort while outperforming existing detection methods. Uncertainty quantification and radiomics features analysis confirmed the interpretability of case prediction after inclusion of EAT features.

eess.IV↗

arXiv2023

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images

Objectives To develop and validate a deep learning-based diagnostic model incorporating uncertainty estimation so as to facilitate radiologists in the preoperative differentiation of the pathological subtypes of renal cell carcinoma (RCC) based on CT images. Methods Data from 668 consecutive patients, pathologically proven RCC, were retrospectively collected from Center 1. By using five-fold cross-validation, a deep learning model incorporating uncertainty estimation was developed to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC). An external validation set of 78 patients from Center 2 further evaluated the model's performance. Results In the five-fold cross-validation, the model's area under the receiver operating characteristic curve (AUC) for the classification of ccRCC, pRCC, and chRCC was 0.868 (95% CI: 0.826-0.923), 0.846 (95% CI: 0.812-0.886), and 0.839 (95% CI: 0.802-0.88), respectively. In the external validation set, the AUCs were 0.856 (95% CI: 0.838-0.882), 0.787 (95% CI: 0.757-0.818), and 0.793 (95% CI: 0.758-0.831) for ccRCC, pRCC, and chRCC, respectively. Conclusions The developed deep learning model demonstrated robust performance in predicting the pathological subtypes of RCC, while the incorporated uncertainty emphasized the importance of understanding model confidence, which is crucial for assisting clinical decision-making for patients with renal tumors. Clinical relevance statement Our deep learning approach, integrated with uncertainty estimation, offers clinicians a dual advantage: accurate RCC subtype predictions complemented by diagnostic confidence references, promoting informed decision-making for patients with RCC.

eess.IV↗

arXiv2023

Generation of Kerr soliton microcomb in a normally dispersed lithium niobate microdisk resonator by mode trimming

Anomalous microresonator dispersion is mandatory for Kerr soliton microcomb formation, which depends critically on the geometry of the microresonator and can hardly be tuned after the structure is made. To date, cavity-based microcombs have only been generated with fundamental whispering gallery modes (WGMs) of anomalous dispersion in microresonators. Moreover, microcomb generation in highly Raman-active platforms such as lithium niobate (LN) microresonators frequently suffers from stimulated Raman scattering and mode crossing due to the existence of multiple families of high-order WGMs. Here, we reveal a unique Kerr soliton microcomb generation mechanism through mode trimming in a weakly perturbed LN microdisk resonator. Remarkably, the soliton comb is generated with fundamental WGMs of normal dispersion and free from the mode crossing and Raman scattering effects. A robust soliton with a spectrum spanning from 1450 nm to 1620 nm at an on-chip pump power of 35 mW. Our discovery offers a powerful solution to circumvent the stringent requirements on high-precision dispersion engineering and termination of Raman excitation for soliton generation in the high-Q microdisk.

physics.optics↗

arXiv2023

MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation

The privacy protection mechanism of federated learning (FL) offers an effective solution for cross-center medical collaboration and data sharing. In multi-site medical image segmentation, each medical site serves as a client of FL, and its data naturally forms a domain. FL supplies the possibility to improve the performance of seen domains model. However, there is a problem of domain generalization (DG) in the actual de-ployment, that is, the performance of the model trained by FL in unseen domains will decrease. Hence, MLA-BIN is proposed to solve the DG of FL in this study. Specifically, the model-level attention module (MLA) and batch-instance style normalization (BIN) block were designed. The MLA represents the unseen domain as a linear combination of seen domain models. The atten-tion mechanism is introduced for the weighting coefficient to obtain the optimal coefficient ac-cording to the similarity of inter-domain data features. MLA enables the global model to gen-eralize to unseen domain. In the BIN block, batch normalization (BN) and instance normalization (IN) are combined to perform the shallow layers of the segmentation network for style normali-zation, solving the influence of inter-domain image style differences on DG. The extensive experimental results of two medical image seg-mentation tasks demonstrate that the proposed MLA-BIN outperforms state-of-the-art methods.

eess.IV↗

arXiv2023

Composite picosecond control of atomic state through a nanofiber interface

Atoms are ideal quantum sensors and quantum light emitters. Interfacing atoms with nanophotonic devices promises novel nanoscale sensing and quantum optical functionalities. But precise optical control of atomic states in these devices is challenged by the spatially varying light-atom coupling strength, generic to nanophotonic. We demonstrate numerically that despite the inhomogenuity, composite picosecond optical pulses with optimally tailored phases are able to evanescently control the atomic electric dipole transitions nearly perfectly, with $f>99\%$ fidelity across large enough volumes for {\it e.g.} controlling cold atoms confined in near-field optical lattices. Our proposal is followed by a proof-of-principle demonstration with a $^{85}$Rb vapor -- optical nanofiber interface, where the excitation by an $N=3$ sequence of guided picosecond D1 control reduces the absorption of a co-guided nanosecond D2 probe by up to $\sim70\%$. The close-to-ideal performance is corroborated by comparing the absorption data across the parameter space with first-principle modeling of the mesoscopic atomic vapor response. Extension of the composite technique to $N\geq 5$ appears highly feasible to support arbitrary local control of atomic dipoles with exquisite precision. This unprecedented ability would allow error-resilient atomic spectroscopy and open up novel nonlinear quantum optical research with atom-nanophotonic interfaces.

physics.optics↗

arXiv2022

Modes trimming and clustering in a weakly perturbed high-Q whispering gallery microresonator

In general, a high-Q microresonator can accommodate abundant whispering gallery modes (WGMs) with the mode number increasing with the dimensional sizes of the microresonator. Removing the unnecessary modes while reorganizing the remaining modes is of vital importance, which, however, has been proved challenging and usually results in a tradeoff with the Q of the microresonator. Here, we reveal an effective and controllable mode trimming and clustering mechanism underlying the generation of polygon and star modes in weakly perturbed tapered fiber-coupled lithium niobate whispering gallery microresonators. Experimentally, various polygon and star modes are observed in sequence within a single microresonator by tuning the excitation wavelength or varying the coupling position between a tapered fiber and the circular microresonator, which can be well reproduced with our theoretical model. The finding offers a ubiquitous solution for a broad range of applications requiring elaborate selection and organization of the high-Q WGMs.

physics.optics↗

arXiv2022

Electro-optically tunable low phase-noise microwave synthesizer in an active lithium niobate microdisk

Photonic-based low-phase-noise microwave generation with real-time frequency tuning is crucial for a broad spectrum of subjects, including next-generation wireless communications, radar, metrology, and modern instrumentation. Here, for the first time to the best of our knowledge, narrow-bandwidth dual-wavelength microlasers are generated from nearly degenerate polygon modes in a high-Q active lithium niobate microdisk. The high-Q polygon modes formation with independently controllable resonant wavelengths and free spectral ranges is enabled by the weak perturbation of the whispering gallery microdisk resonators using a tapered fiber. The stable beating signal confirms the low phase-noise achieved in the tunable laser. Owing to the high spatial overlap factors between the two nearly degenerate lasing modes as well as that between the two lasing modes and the pump mode, gain competition between the two modes is suppressed, leading to stable dual-wavelength laser generation and in turn the low noise microwave source. The measured microwave signal shows a linewidth of ~6.87 kHz, a phase noise of ~-123 dBc/Hz, and an electro-optic tuning efficiency of -1.66 MHz/V.

physics.optics↗

arXiv2021

Coherent mode-combined ultra-narrow-linewidth single-mode micro-disk laser

Integrated single-mode microlasers with ultra-narrow linewidths play a game-changing role in a broad spectrum of applications ranging from coherent communication and LIDAR to metrology and sensing. Generation of such light sources in a controllable and cost-effective manner remains an outstanding challenge due to the difficulties in the realization of ultra-high Q active micro-resonators with suppressed mode numbers. Here, we report a microlaser generated in an ultra-high Q Erbium doped lithium niobate (LN) micro-disk. Through the formation of coherently combined polygon modes at both pump and laser wavelengths, the microlaser exhibits single mode operation with an ultra-narrow-linewidth of 98 Hz. In combination with the superior electro-optic and nonlinear optical properties of LN crystal, the mass-producible on-chip single-mode microlaser will provide an essential building block for the photonic integrated circuits demanding high precision frequency control and reconfigurability.

physics.optics↗

arXiv2021

Lithium niobate microring with ultra-high Q factor above 10^8

We demonstrate ultra-high Q factor microring resonators close to the intrinsic material absorption limit on lithium niobate on insulator. The microrings are fabricated on pristine lithium niobate (LN) thin film wafer thinned from LN bulk via chemo-mechanical etching without ion slicing and ion etching. A record-high Q factor up to times ten to the power of 8th at the wavelength of 1550 nm is achieved because of the ultra-smooth interface of the microrings and the absence of ion induced lattice damage, indicating an ultra-low waveguide propagation loss of about 0.28 dB per meter. The ultra-high Q microrings will pave the way for integrated quantum light source, frequency comb generation, and nonlinear optical processes.

physics.optics↗

arXiv2021

On-chip ultra-narrow-linewidth single-mode microlaser on lithium niobate on insulator

We report an on-chip single mode microlaser with low-threshold fabricated on Erbium doped lithium niobate on insulator (LNOI). The single mode laser emission at 1550.5 nm wavelength is generated in a coupled photonic molecule, which is facilitated by Vernier effect when pumping the photonic molecule at 970 nm. A threshold pump power as low as 200 uW is demonstrated thanks to the high quality factor above 10^6. Moreover, the linewidth of the microlaser reaches 4 kHz, which is the best result in LNOI microlasers. Such single mode micro-laser lithographically fabricated on chip is highly in demand by photonic community.

physics.optics↗

arXiv2021

Broadband highly efficient nonlinear optical processes in on-chip integrated lithium niobate microdisk resonators of Q-factor above 10^8

We demonstrated broadband highly efficient optical nonlinear processes in on-chip integrated lithium niobate (LN) microdisk resonators. The Q factors of the micro-resonators fabricated by femtosecond laser writing and chemo-mechanical polishing are reliably above 10^8, approaching the intrinsic material absorption limit of LN. Broadband nonlinear processes, including optical parametric oscillation (OPO), second harmonic generation (SHG), third harmonic generation, and fourth harmonic generation, were observed with ultrahigh efficiencies in the same LN microdisk without introducing domain inversion, thanks to the natural quasi phase-matching and the dense spectral modes of the X-cut LN microdisk with millimeter diameter. The threshold of OPO and the absolute conversion efficiency of SHG are 19.6 microwatt and 66%, both surpass the state-of-the-art values among on-chip LN micro-resonators demonstrated so far. The broadband and highly efficient nonlinear frequency conversions achieved with the ultrahigh-Q LN microdisk resonators promise high-density integration of nonlinear photonic devices such as frequency convertors and entangled photon sources.

physics.optics↗