arXiv subjects

Shanshan Wang

Publications and source records attributed to Shanshan Wang.

At least 109 records · Page 6Linked to original sources

arXiv2024

Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining

Accurate medical image segmentation demands the integration of multi-scale information, spanning from local features to global dependencies. However, it is challenging for existing methods to model long-range global information, where convolutional neural networks (CNNs) are constrained by their local receptive fields, and vision transformers (ViTs) suffer from high quadratic complexity of their attention mechanism. Recently, Mamba-based models have gained great attention for their impressive ability in long sequence modeling. Several studies have demonstrated that these models can outperform popular vision models in various tasks, offering higher accuracy, lower memory consumption, and less computational burden. However, existing Mamba-based models are mostly trained from scratch and do not explore the power of pretraining, which has been proven to be quite effective for data-efficient medical image analysis. This paper introduces a novel Mamba-based model, Swin-UMamba, designed specifically for medical image segmentation tasks, leveraging the advantages of ImageNet-based pretraining. Our experimental results reveal the vital role of ImageNet-based training in enhancing the performance of Mamba-based models. Swin-UMamba demonstrates superior performance with a large margin compared to CNNs, ViTs, and latest Mamba-based models. Notably, on AbdomenMRI, Encoscopy, and Microscopy datasets, Swin-UMamba outperforms its closest counterpart U-Mamba_Enc by an average score of 2.72%.

eess.IV↗

arXiv2024

Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques

The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications. While previous studies have commonly focused on feature learning at a single learning scale, investigation on integrating multi-scale information is lacking, which may hinder the potential for mutual reinforcement among these features. This paper aims to bridge this gap by proposing a method that effectively exploits multi-scale information to enhance the performance of medical foundation models. The proposed method simultaneously exploits features at the local, instance, modality and global aspects, facilitating comprehensive representation learning within the models. We evaluate the effectiveness of the proposed method on six open-source datasets across different clinical tasks, demonstrating its ability to enhance the performance of medical foundation models.

cs.CV↗

arXiv2024

Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning

Deep learning (DL) has emerged as a leading approach in accelerating MR imaging. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MR imaging involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MR imaging along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.

eess.IV↗

arXiv2024

Positive and negative sampling strategies for self-supervised learning on audio-video data

In Self-Supervised Learning (SSL), Audio-Visual Correspondence (AVC) is a popular task to learn deep audio and video features from large unlabeled datasets. The key step in AVC is to randomly sample audio and video clips from the dataset and learn to minimize the feature distance between the positive pairs (corresponding audio-video pair) while maximizing the distance between the negative pairs (non-corresponding audio-video pairs). The learnt features are shown to be effective on various downstream tasks. However, these methods achieve subpar performance when the size of the dataset is rather small. In this paper, we investigate the effect of utilizing class label information in the AVC feature learning task. We modified various positive and negative data sampling techniques of SSL based on class label information to investigate the effect on the feature quality. We propose a new sampling approach which we call soft-positive sampling, where the positive pair for one audio sample is not from the exact corresponding video, but from a video of the same class. Experimental results suggest that when the dataset size is small in SSL setup, features learnt through the soft-positive sampling method significantly outperform those from the traditional SSL sampling approaches. This trend holds in both in-domain and out-of-domain downstream tasks, and even outperforms supervised classification. Finally, experiments show that class label information can easily be obtained using a publicly available classifier network and then can be used to boost the SSL performance without adding extra data annotation burden.

eess.AS↗

arXiv2024

MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning

Existing contrastive language-image pre-training aims to learn a joint representation by matching abundant image-text pairs. However, the number of image-text pairs in medical datasets is usually orders of magnitude smaller than that in natural datasets. Besides, medical image-text pairs often involve numerous complex fine-grained correspondences. This paper aims to enhance the data efficiency by introducing multiple-to-multiple local relationship modeling to capture denser supervisions. More specifically, we propose a Medical Language-Image Pre-training (MLIP) framework, which exploits the limited image-text medical data more efficiently through patch-sentence matching. Furthermore, we introduce a masked contrastive learning strategy with semantic integrity estimation to reduce redundancy in images while preserving the underlying semantics. Our evaluation results show that MLIP outperforms previous work in zero/few-shot classification and few-shot segmentation tasks by a large margin.

cs.CV↗

arXiv2024

LESEN: Label-Efficient deep learning for Multi-parametric MRI-based Visual Pathway Segmentation

Recent research has shown the potential of deep learning in multi-parametric MRI-based visual pathway (VP) segmentation. However, obtaining labeled data for training is laborious and time-consuming. Therefore, it is crucial to develop effective algorithms in situations with limited labeled samples. In this work, we propose a label-efficient deep learning method with self-ensembling (LESEN). LESEN incorporates supervised and unsupervised losses, enabling the student and teacher models to mutually learn from each other, forming a self-ensembling mean teacher framework. Additionally, we introduce a reliable unlabeled sample selection (RUSS) mechanism to further enhance LESEN's effectiveness. Our experiments on the human connectome project (HCP) dataset demonstrate the superior performance of our method when compared to state-of-the-art techniques, advancing multimodal VP segmentation for comprehensive analysis in clinical and research settings. The implementation code will be available at: https://github.com/aldiak/Semi-Supervised-Multimodal-Visual-Pathway- Delineation.

eess.IV↗

arXiv2024

Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the \textit{in vivo} human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying $l1$-norm and total-variation regularization. The experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise.

cs.CV↗

arXiv2024

Modality Exchange Network for Retinogeniculate Visual Pathway Segmentation

Accurate segmentation of the retinogeniculate visual pathway (RGVP) aids in the diagnosis and treatment of visual disorders by identifying disruptions or abnormalities within the pathway. However, the complex anatomical structure and connectivity of RGVP make it challenging to achieve accurate segmentation. In this study, we propose a novel Modality Exchange Network (ME-Net) that effectively utilizes multi-modal magnetic resonance (MR) imaging information to enhance RGVP segmentation. Our ME-Net has two main contributions. Firstly, we introduce an effective multi-modal soft-exchange technique. Specifically, we design a channel and spatially mixed attention module to exchange modality information between T1-weighted and fractional anisotropy MR images. Secondly, we propose a cross-fusion module that further enhances the fusion of information between the two modalities. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches in terms of RGVP segmentation performance.

eess.IV↗

arXiv2024

AID-DTI: Accelerating High-fidelity Diffusion Tensor Imaging with Detail-Preserving Model-based Deep Learning

Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and detail loss in reconstructing the DTI-derived parametric maps especially when sparsely sampled q-space data are used. This paper proposes a novel method, AID-DTI (Accelerating hIgh fiDelity Diffusion Tensor Imaging), to facilitate fast and accurate DTI with only six measurements. AID-DTI is equipped with a newly designed Singular Value Decomposition (SVD)-based regularizer, which can effectively capture fine details while suppressing noise during network training. Experimental results on Human Connectome Project (HCP) data consistently demonstrate that the proposed method estimates DTI parameter maps with fine-grained details and outperforms three state-of-the-art methods both quantitatively and qualitatively.

cs.CV↗

arXiv2023

Congestions and Spectral Transition in Time-Lagged Correlations of Motorway Traffic

The congestion of a motorway section is propagated to its neighbouring sections, leading to correlations. The resulting correlation matrix encodes the information on congestion. Here, we study symmetrized time-lagged correlations and show how their spectral properties reveal congestion durations. We carry out an empirical analysis and find a transition behavior for the dominant eigenvalue as function of the time lag. Furthermore, we set up a numerical simulation model for indicator time series of traffic phases as well as a simplified model that we treat analytically. We consider various scenarios. Our results reveal a nonlinear relation between the spectral transition and the congestion duration. In our simplified model, we derive this relation analytically.

physics.soc-ph↗

arXiv2023

Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation

Background: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating 3D MR images is tedious and time-consuming, requiring experts with rich domain knowledge and experience. Purpose: To build a deep learning method exploring sparse annotations, namely only a single 2D slice label for each 3D training MR image. Population: 3D MR images of 150 subjects from two publicly available datasets were included. Among them, 50 (1,377 image slices) are for prostate segmentation. The other 100 (8,800 image slices) are for left atrium segmentation. Five-fold cross-validation experiments were carried out utilizing the first dataset. For the second dataset, 80 subjects were used for training and 20 were used for testing. Assessment: A collaborative learning method by integrating the strengths of semi-supervised and self-supervised learning schemes was developed. The method was trained using labeled central slices and unlabeled non-central slices. Segmentation performance on testing set was reported quantitatively and qualitatively. Results: Compared to FS-LCS, MT, UA-MT, DCT-Seg, ICT, and AC-MT, the proposed method achieved a substantial improvement in segmentation accuracy, increasing the mean B-IoU significantly by more than 10.0% for prostate segmentation (proposed method B-IoU: 70.3% vs. ICT B-IoU: 60.3%) and by more than 6.0% for left atrium segmentation (proposed method B-IoU: 66.1% vs. ICT B-IoU: 60.1%).

eess.IV↗

arXiv2023

Metastability and anharmonicity enhance defect-assisted nonradiative recombination in low-symmetry semiconductors

Strong nonradiative recombination has been observed in quasi-one-dimensional antimony selenide, which runs counter to the simple intuition that claims high defect tolerance exists in semiconductors with antibonding state in the valence band and bonding state in the conduction band. Here we reveal such a defect intolerance actually stems from the richness of structural metastability and vibrational anharmonicity owing to the low-symmetry atomic structure. Taking the deep defect V$_{\rm Se}$ as a benchmark, we show the defect with its ground-state configuration alone does not act as a recombination center. Instead, we identify three different configurations with different formation energies, such richness of metastability offers a higher probability to accomplish a rapid recombination cycle. Another contributing factor is the anharmonicity in the potential energy surfaces that is caused by the large atomic relaxation, which elevates the total capture coefficient by 2-3 orders of magnitude compared with harmonic approximation. Therefore, the unique properties from both crystals and phonons in quasi-one-dimensional system enhance the nonradiative recombination, making the traditional intuition of defect tolerance invalid. These results highlight the importance of the correct identification of metastable defects and phonon anharmonicity in the nonradiative recombination in low-symmetry semiconductors.

cond-mat.mtrl-sci↗

arXiv2023

Partition-based K-space Synthesis for Multi-contrast Parallel Imaging

Multi-contrast magnetic resonance imaging is a significant and essential medical imaging technique.However, multi-contrast imaging has longer acquisition time and is easy to cause motion artifacts. In particular, the acquisition time for a T2-weighted image is prolonged due to its longer repetition time (TR). On the contrary, T1-weighted image has a shorter TR. Therefore,utilizing complementary information across T1 and T2-weighted image is a way to decrease the overall imaging time. Previous T1-assisted T2 reconstruction methods have mostly focused on image domain using whole-based image fusion approaches. The image domain reconstruction method has the defects of high computational complexity and limited flexibility. To address this issue, we propose a novel multi-contrast imaging method called partition-based k-space synthesis (PKS) which can achieve super reconstruction quality of T2-weighted image by feature fusion. Concretely, we first decompose fully-sampled T1 k-space data and under-sampled T2 k-space data into two sub-data, separately. Then two new objects are constructed by combining the two sub-T1/T2 data. After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image. Finally, the objective T2 is synthesized by extracting the sub-T2 data of each part. Experimental results showed that our combined technique can achieve comparable or better results than using traditional k-space parallel imaging(SAKE) that processes each contrast independently.

eess.IV↗

arXiv2023

Delay-Adaptive Compensator for 3-D Space Formation of Multi-Agent Systems with Leaders Actuation

This paper focuses on the control of collective dynamics in large-scale multi-agent systems (MAS) operating in a 3-D space, with a specific emphasis on compensating for the influence of an unknown delay affecting the actuated leaders. The communication graph of the agents is defined on a mesh-grid 2-D cylindrical surface. We model the agents' collective dynamics by a complex- and a real-valued reaction-advection-diffusion 2-D partial differential equations (PDEs) whose states represent the 3-D position coordinates of the agents. The leader agents on the boundary suffer unknown actuator delay due to the cumulative computation and information transmission time. We design a delay-adaptive controller for the 2-D PDE by using PDE backstepping combined with a Lyapunov functional method, where the latter is employed to design an update law that generates real-time estimates of the unknown delay. Capitalizing on our recent result on the control of 1-D parabolic PDEs with unknown input delay, we use Fourier series expansion to bridge the control of 1-D PDEs to that of 2-D PDEs. To design the update law for the 2-D system, a new target system is defined to establish the closed-loop local boundedness of the system trajectories in $H^2$ norm and the regulation of the states to zero assuming a measurement of the spatially distributed plant's state. We illustrate the performance of delay-adaptive controller by numerical simulations.

math.OC↗

arXiv2023

Defect Regulation by Palladium Incorporation towards Grain Boundaries of Kesterite solar cells

Kesterite Cu2ZnSn(S, Se)4 (CZTSSe) solar cell has emerged as one of the most promising candidates for thin-film photovoltaics. However, severe charge losses occurring at the grain boundaries (GBs) of Kesterite polycrystalline absorbers has hindered the improvement of cell performance. Herein, we report a redox reaction strategy involving palladium (Pd) to eliminate atomic vacancy defects such as VSn and VSe in GBs of the Kesterite absorbers. We demonstrate that PdSex compounds could form during the selenization process and distribute at the GBs and the absorber surfaces; thereby aid in the suppression of Sn and Se volatilization loss and inhibiting the formation of VSn and VSe defects. Furthermore, Pd(II)/Pd(IV) serves as a redox shuttle, i.e., on one hand, Pd(II) captures Se vapor from the reaction environment to produce PdSe2, on the other hand, PdSe2 provides Se atoms to the Kesterite absorber by being reduced to PdSe, thus contributing to the elimination of pre-existing VSe defects within GBs. These effects collectively reduce defects and enhance the p-type characteristics of the Kesterite absorber, leading to a significant reduction in charge recombination loss within the cell. As a result, high-performance Kesterite solar cells with a total-area efficiency of 14.5% have been achieved. This remarkable efficiency increase benefited from the redox reaction strategy offers a promising avenue for the precise regulation of defects in Kesterite solar cells and holds generally significant implications for the exploration of various other photovoltaic devices.

cond-mat.mtrl-sci↗

arXiv2023

Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search

Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model. While a centralized training model is effective in mitigating this problem, it raises concerns about privacy protection. Federated learning is a distributed training paradigm that can utilize multi-institutional data for collaborative training without sharing data. However, existing federated learning MR image reconstruction methods rely on models designed manually by experts, which are complex and computational expensive, suffering from performance degradation when facing heterogeneous data distributions. In addition, these methods give inadequate consideration to fairness issues, namely, ensuring that the model's training does not introduce bias towards any specific dataset's distribution. To this end, this paper proposes a generalizable federated neural architecture search framework for accelerating MR imaging (GAutoMRI). Specifically, automatic neural architecture search is investigated for effective and efficient neural network representation learning of MR images from different centers. Furthermore, we design a fairness adjustment approach that can enable the model to learn features fairly from inconsistent distributions of different devices and centers, and thus enforce the model generalize to the unseen center. Extensive experiments show that our proposed GAutoMRI has better performances and generalization ability compared with six state-of-the-art federated learning methods. Moreover, the GAutoMRI model is significantly more lightweight, making it an efficient choice for MR image reconstruction tasks. The code will be made available at https://github.com/ternencewu123/GAutoMRI.

eess.IV↗

arXiv2023

Efficient Learned Lossless JPEG Recompression

JPEG is one of the most popular image compression methods. It is beneficial to compress those existing JPEG files without introducing additional distortion. In this paper, we propose a deep learning based method to further compress JPEG images losslessly. Specifically, we propose a Multi-Level Parallel Conditional Modeling (ML-PCM) architecture, which enables parallel decoding in different granularities. First, luma and chroma are processed independently to allow parallel coding. Second, we propose pipeline parallel context model (PPCM) and compressed checkerboard context model (CCCM) for the effective conditional modeling and efficient decoding within luma and chroma components. Our method has much lower latency while achieves better compression ratio compared with previous SOTA. After proper software optimization, we can obtain a good throughput of 57 FPS for 1080P images on NVIDIA T4 GPU. Furthermore, combined with quantization, our approach can also act as a lossy JPEG codec which has obvious advantage over SOTA lossy compression methods in high bit rate (bpp$>0.9$).

eess.IV↗

arXiv2023

Deep Learning of Delay-Compensated Backstepping for Reaction-Diffusion PDEs

Deep neural networks that approximate nonlinear function-to-function mappings, i.e., operators, which are called DeepONet, have been demonstrated in recent articles to be capable of encoding entire PDE control methodologies, such as backstepping, so that, for each new functional coefficient of a PDE plant, the backstepping gains are obtained through a simple function evaluation. These initial results have been limited to single PDEs from a given class, approximating the solutions of only single-PDE operators for the gain kernels. In this paper we expand this framework to the approximation of multiple (cascaded) nonlinear operators. Multiple operators arise in the control of PDE systems from distinct PDE classes, such as the system in this paper: a reaction-diffusion plant, which is a parabolic PDE, with input delay, which is a hyperbolic PDE. The DeepONet-approximated nonlinear operator is a cascade/composition of the operators defined by one hyperbolic PDE of the Goursat form and one parabolic PDE on a rectangle, both of which are bilinear in their input functions and not explicitly solvable. For the delay-compensated PDE backstepping controller, which employs the learned control operator, namely, the approximated gain kernel, we guarantee exponential stability in the $L^2$ norm of the plant state and the $H^1$ norm of the input delay state. Simulations illustrate the contributed theory.

math.AP↗