arXiv subjects

Ge Wang

Publications and source records attributed to Ge Wang.

At least 181 records · Page 10Linked to original sources

arXiv2022

Quasi-supervised Learning for Super-resolution PET

Low resolution of positron emission tomography (PET) limits its diagnostic performance. Deep learning has been successfully applied to achieve super-resolution PET. However, commonly used supervised learning methods in this context require many pairs of low- and high-resolution (LR and HR) PET images. Although unsupervised learning utilizes unpaired images, the results are not as good as that obtained with supervised deep learning. In this paper, we propose a quasi-supervised learning method, which is a new type of weakly-supervised learning methods, to recover HR PET images from LR counterparts by leveraging similarity between unpaired LR and HR image patches. Specifically, LR image patches are taken from a patient as inputs, while the most similar HR patches from other patients are found as labels. The similarity between the matched HR and LR patches serves as a prior for network construction. Our proposed method can be implemented by designing a new network or modifying an existing network. As an example in this study, we have modified the cycle-consistent generative adversarial network (CycleGAN) for super-resolution PET. Our numerical and experimental results qualitatively and quantitatively show the merits of our method relative to the state-ofthe-art methods. The code is publicly available at https://github.com/PigYang-ops/CycleGAN-QSDL.

eess.IV↗

arXiv2022

DLME: Deep Local-flatness Manifold Embedding

Manifold learning (ML) aims to seek low-dimensional embedding from high-dimensional data. The problem is challenging on real-world datasets, especially with under-sampling data, and we find that previous methods perform poorly in this case. Generally, ML methods first transform input data into a low-dimensional embedding space to maintain the data's geometric structure and subsequently perform downstream tasks therein. The poor local connectivity of under-sampling data in the former step and inappropriate optimization objectives in the latter step leads to two problems: structural distortion and underconstrained embedding. This paper proposes a novel ML framework named Deep Local-flatness Manifold Embedding (DLME) to solve these problems. The proposed DLME constructs semantic manifolds by data augmentation and overcomes the structural distortion problem using a smoothness constrained based on a local flatness assumption about the manifold. To overcome the underconstrained embedding problem, we design a loss and theoretically demonstrate that it leads to a more suitable embedding based on the local flatness. Experiments on three types of datasets (toy, biological, and image) for various downstream tasks (classification, clustering, and visualization) show that our proposed DLME outperforms state-of-the-art ML and contrastive learning methods.

cs.LG↗

arXiv2022

HOME: High-Order Mixed-Moment-based Embedding for Representation Learning

Minimum redundancy among different elements of an embedding in a latent space is a fundamental requirement or major preference in representation learning to capture intrinsic informational structures. Current self-supervised learning methods minimize a pair-wise covariance matrix to reduce the feature redundancy and produce promising results. However, such representation features of multiple variables may contain the redundancy among more than two feature variables that cannot be minimized via the pairwise regularization. Here we propose the High-Order Mixed-Moment-based Embedding (HOME) strategy to reduce the redundancy between any sets of feature variables, which is to our best knowledge the first attempt to utilize high-order statistics/information in this context. Multivariate mutual information is minimum if and only if multiple variables are mutually independent, which suggests the necessary conditions of factorized mixed moments among multiple variables. Based on these statistical and information theoretic principles, our general HOME framework is presented for self-supervised representation learning. Our initial experiments show that a simple version in the form of a three-order HOME scheme already significantly outperforms the current two-order baseline method (i.e., Barlow Twins) in terms of the linear evaluation on representation features.

cs.LG↗

arXiv2022

ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning

Contrastive Learning (CL) has emerged as a dominant technique for unsupervised representation learning which embeds augmented versions of the anchor close to each other (positive samples) and pushes the embeddings of other samples (negatives) apart. As revealed in recent studies, CL can benefit from hard negatives (negatives that are most similar to the anchor). However, we observe limited benefits when we adopt existing hard negative mining techniques of other domains in Graph Contrastive Learning (GCL). We perform both experimental and theoretical analysis on this phenomenon and find it can be attributed to the message passing of Graph Neural Networks (GNNs). Unlike CL in other domains, most hard negatives are potentially false negatives (negatives that share the same class with the anchor) if they are selected merely according to the similarities between anchor and themselves, which will undesirably push away the samples of the same class. To remedy this deficiency, we propose an effective method, dubbed \textbf{ProGCL}, to estimate the probability of a negative being true one, which constitutes a more suitable measure for negatives' hardness together with similarity. Additionally, we devise two schemes (i.e., \textbf{ProGCL-weight} and \textbf{ProGCL-mix}) to boost the performance of GCL. Extensive experiments demonstrate that ProGCL brings notable and consistent improvements over base GCL methods and yields multiple state-of-the-art results on several unsupervised benchmarks or even exceeds the performance of supervised ones. Also, ProGCL is readily pluggable into various negatives-based GCL methods for performance improvement. We release the code at \textcolor{magenta}{\url{https://github.com/junxia97/ProGCL}}.

cs.AI↗

arXiv2022

Self-Supervised Representation Learning With MUlti-Segmental Informational Coding (MUSIC)

Self-supervised representation learning maps high-dimensional data into a meaningful embedding space, where samples of similar semantic contents are close to each other. Most of the recent representation learning methods maximize cosine similarity or minimize the distance between the embedding features of different views from the same sample usually on the $l2$ normalized unit hypersphere. To prevent the trivial solutions that all samples have the same embedding feature, various techniques have been developed, such as contrastive learning, stop gradient, variance and covariance regularization, etc. In this study, we propose MUlti-Segmental Informational Coding (MUSIC) for self-supervised representation learning. MUSIC divides the embedding feature into multiple segments that discriminatively partition samples into different semantic clusters and different segments focus on different partition principles. Information theory measurements are directly used to optimize MUSIC and theoretically guarantee trivial solutions are avoided. MUSIC does not depend on commonly used techniques, such as memory bank or large batches, asymmetry networks, gradient stopping, momentum weight updating, etc, making the training framework flexible. Our experiments demonstrate that MUSIC achieves better results than most related Barlow Twins and VICReg methods on ImageNet classification with linear probing, and requires neither deep projectors nor large feature dimensions. Code will be made available.

cs.CV↗

arXiv2022

GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection

In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images. GasHis-Transformer model consists of two key modules designed to extract global and local information using a position-encoded transformer model and a convolutional neural network with local convolution, respectively. A publicly available hematoxylin and eosin (H&E) stained gastric histopathological image dataset is used in the experiment. Furthermore, a Dropconnect based lightweight network is proposed to reduce the model size and training time of GasHis-Transformer for clinical applications with improved confidence. Moreover, a series of contrast and extended experiments verify the robustness, extensibility and stability of GasHis-Transformer. In conclusion, GasHis-Transformer demonstrates high global detection performance and shows its significant potential in GHID task.

cs.CV↗

arXiv2022

SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image Analysis

Recently, deep learning has achieved remarkable successes in medical image analysis. Although deep neural networks generate clinically important predictions, they have inherent uncertainty. Such uncertainty is a major barrier to report these predictions with confidence. In this paper, we propose a novel yet simple Bayesian inference approach called SoftDropConnect (SDC) to quantify the network uncertainty in medical imaging tasks with gliomas segmentation and metastases classification as initial examples. Our key idea is that during training and testing SDC modulates network parameters continuously so as to allow affected information processing channels still in operation, instead of disabling them as Dropout or DropConnet does. When compared with three popular Bayesian inference methods including Bayes By Backprop, Dropout, and DropConnect, our SDC method (SDC-W after optimization) outperforms the three competing methods with a substantial margin. Quantitatively, our proposed method generates substantial improvements in prediction accuracy (by 3.4%, 2.5%, and 6.7% respectively for whole tumor segmentation in terms of dice score; and by 11.7%, 3.9%, and 8.7% respectively for brain metastases classification) and greatly reduced epistemic and aleatoric uncertainties. Our approach promises to deliver better diagnostic performance and make medical AI imaging more explainable and trustworthy.

eess.IV↗

arXiv2022

Quasi-Equivalence of Width and Depth of Neural Networks

While classic studies proved that wide networks allow universal approximation, recent research and successes of deep learning demonstrate the power of deep networks. Based on a symmetric consideration, we investigate if the design of artificial neural networks should have a directional preference, and what the mechanism of interaction is between the width and depth of a network. Inspired by the De Morgan law, we address this fundamental question by establishing a quasi-equivalence between the width and depth of ReLU networks in two aspects. First, we formulate two transforms for mapping an arbitrary ReLU network to a wide network and a deep network respectively for either regression or classification so that the essentially same capability of the original network can be implemented. Then, we replace the mainstream artificial neuron type with a quadratic counterpart, and utilize the factorization and continued fraction representations of the same polynomial function to construct a wide network and a deep network, respectively. Based on our findings, a deep network has a wide equivalent, and vice versa, subject to an arbitrarily small error.

cs.LG↗

arXiv2022

Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection

Early detection of lung nodules with computed tomography (CT) is critical for the longer survival of lung cancer patients and better quality of life. Computer-aided detection/diagnosis (CAD) is proven valuable as a second or concurrent reader in this context. However, accurate detection of lung nodules remains a challenge for such CAD systems and even radiologists due to not only the variability in size, location, and appearance of lung nodules but also the complexity of lung structures. This leads to a high false-positive rate with CAD, compromising its clinical efficacy. Motivated by recent computer vision techniques, here we present a self-supervised region-based 3D transformer model to identify lung nodules among a set of candidate regions. Specifically, a 3D vision transformer (ViT) is developed that divides a CT image volume into a sequence of non-overlap cubes, extracts embedding features from each cube with an embedding layer, and analyzes all embedding features with a self-attention mechanism for the prediction. To effectively train the transformer model on a relatively small dataset, the region-based contrastive learning method is used to boost the performance by pre-training the 3D transformer with public CT images. Our experiments show that the proposed method can significantly improve the performance of lung nodule screening in comparison with the commonly used 3D convolutional neural networks.

cs.CV↗

arXiv2022

Research Status of Deep Learning Methods for Rumor Detection

To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks. To comprehensively sort out the research status of rumor detection from multiple perspectives, this paper analyzes the highly focused work from three perspectives: Feature Selection, Model Structure, and Research Methods. From the perspective of feature selection, we divide methods into content feature, social feature, and propagation structure feature of the rumors. Then, this work divides deep learning models of rumor detection into CNN, RNN, GNN, Transformer based on the model structure, which is convenient for comparison. Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods, based knowledge graph, and other methods for the first time. And compare the advantages of different methods to detect rumors. In addition, this review enumerate datasets available and discusses the potential issues and future work to help researchers advance the development of field.

cs.CY↗

arXiv2022

A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI

Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MRI data. Our IRB-approved single-site retrospective study was comprised of patients (n=1,399) referred for MRI treatment-planning and gamma knife radiosurgery over 21 years. Contrast-enhanced T1-weighted and T2-weighted Fluid-Attenuated Inversion Recovery brain MRI exams (n=1,582) were preprocessed and input to the proposed deep learning workflow for tumor segmentation, modality transfer, and primary site classification into one of five classes. Ten-fold cross-validation generated overall AUC of 0.878 (95%CI:0.873,0.883), lung class AUC of 0.889 (95%CI:0.883,0.895), breast class AUC of 0.873 (95%CI:0.860,0.886), melanoma class AUC of 0.852 (95%CI:0.842,0.862), renal class AUC of 0.830 (95%CI:0.809,0.851), and other class AUC of 0.822 (95%CI:0.805,0.839). These data establish that whole-brain imaging features are discriminative to allow accurate diagnosis of the primary organ site of malignancy. Our end-to-end deep radiomic approach has great potential for classifying metastatic tumor types from whole-brain MRI images. Further refinement may offer an invaluable clinical tool to expedite primary cancer site identification for precision treatment and improved outcomes.

eess.IV↗

arXiv2022

Motion Correction via Locally Linear Embedding for Helical Photon-counting CT

X-ray photon-counting detector (PCD) offers low noise, high resolution, and spectral characterization, representing a next generation of CT and enabling new biomedical applications. It is well known that involuntary patient motion may induce image artifacts with conventional CT scanning, and this problem becomes more serious with PCD due to its high detector pitch and extended scan time. Furthermore, PCD often comes with a substantial number of bad pixels, making analytic image reconstruction challenging and ruling out state-of-the-art motion correction methods that are based on analytical reconstruction. In this paper, we extend our previous locally linear embedding (LLE) cone-beam motion correction method to the helical scanning geometry, which is especially desirable given the high cost of large-area PCD. In addition to our adaption of LLE-based parametric searching to helical cone-beam photon-counting CT geometry, we introduce an unreliable-volume mask to improve the motion estimation accuracy and perform incremental updating on gradually refined sampling grids for optimization of both accuracy and efficiency. Our numerical results demonstrate that our method reduces the estimation errors near the two longitudinal ends of the reconstructed volume and overall image quality. The experimental results on clinical photon-counting scans of the patient extremities show significant resolution improvement after motion correction using our method, which reveals subtle fine structures previously hidden under motion blurring and artifacts.

physics.med-ph↗

arXiv2022

Top-level Design and Simulated Performance of the First Portable CT-MR scanner

Multi-modality imaging hardware can be integrated in a single gantry to collect diverse datasets for complementary information and spatiotemporal correlation, with excellent examples including PET-CT and PET-MRI. However, there is no CT-MRI prototype up to today due to technical challenges and associated cost-benefit considerations. Thanks to the rapid development of medical imaging, it becomes feasible now to integrate cost-effective CT and MRI imagers together for portability, popularity, and point of care. In this paper, we present the top-level design of the first portable CT-MRI system and evaluate its imaging performance via realistic numerical simulations. In this CT-MRI system, the magnet made of two NdFeB rings of about 40.0 cm radius forms a magnetic field of about 57 mT at the isocenter and has a gap of 11.3 cm to accommodate the rotating CT gantry. The targeted MR imaging field of view (FOV) is a sphere of ~15 cm in diameter and that of CT is approximately 20 cm diameter in axial direction and 5 cm in longitudinal direction. Our results show a great potential of such a hybrid system. The proposed CT-MRI system will be valuable in applications such as imaging in underdeveloped countries, disaster scenes and battle fields.

physics.med-ph↗

arXiv2022

X-ray Dissectography Improves Lung Nodule Detection

Although radiographs are the most frequently used worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often renders suspicious or concerning lung nodules difficult to detect. In this study, we apply "X-ray dissectography" to dissect lungs digitally from a few radiographic projections, suppress the interference of irrelevant structures, and improve lung nodule detectability. For this purpose, a collaborative detection network is designed to localize lung nodules in 2D dissected projections and 3D physical space. Our experimental results show that our approach can significantly improve the average precision by 20+% in comparison with the common baseline that detects lung nodules from original projections using a popular detection network. Potentially, this approach could help re-design the current X-ray imaging protocols and workflows and improve the diagnostic performance of chest radiographs in lung diseases.

eess.IV↗

arXiv2022

Convolutional Neural Network to Restore Low-Dose Digital Breast Tomosynthesis Projections in a Variance Stabilization Domain

Digital breast tomosynthesis (DBT) exams should utilize the lowest possible radiation dose while maintaining sufficiently good image quality for accurate medical diagnosis. In this work, we propose a convolution neural network (CNN) to restore low-dose (LD) DBT projections to achieve an image quality equivalent to a standard full-dose (FD) acquisition. The proposed network architecture benefits from priors in terms of layers that were inspired by traditional model-based (MB) restoration methods, considering a model-based deep learning approach, where the network is trained to operate in the variance stabilization transformation (VST) domain. To accurately control the network operation point, in terms of noise and blur of the restored image, we propose a loss function that minimizes the bias and matches residual noise between the input and the output. The training dataset was composed of clinical data acquired at the standard FD and low-dose pairs obtained by the injection of quantum noise. The network was tested using real DBT projections acquired with a physical anthropomorphic breast phantom. The proposed network achieved superior results in terms of the mean normalized squared error (MNSE), training time and noise spatial correlation compared with networks trained with traditional data-driven methods. The proposed approach can be extended for other medical imaging application that requires LD acquisitions.

eess.IV↗

arXiv2022

Stationary Multi-source AI-powered Real-time Tomography (SMART)

Over the past decades, the development of CT technologies has been largely driven by the needs for cardiac imaging but the temporal resolution remains insufficient for clinical CT in difficult cases and rather challenging for preclinical micro-CT since small animals, as human cardiac disease models, have much higher heart rates than human. To address this challenge, here we report a Stationary Multi-source AI-based Real-time Tomography (SMART) micro-CT system. This unique scanner is featured by 29 source-detector pairs fixed on a circular track to collect x-ray signals in parallel, enabling instantaneous tomography in principle. Given the multi-source architecture, the field-of-view only covers a cardiac region. To solve this interior problem, an AI-empowered interior tomography approach is developed to synergize sparsity-based regularization and learning-based reconstruction. To demonstrate the performance and utilities of the SMART system, extensive results are obtained in physical phantom experiments and animal studies, including dead and live rats as well as live rabbits. The reconstructed volumetric images convincingly demonstrate the merits of the SMART system using the AI-empowered interior tomography approach, enabling cardiac micro-CT with the unprecedented temporal resolution of 30ms, which is an order of magnitude higher than the state of the art.

q-bio.QM↗

arXiv2022

Unravelling Distance-Dependent Inter-Site Interactions and Magnetic Transition Effects of Heteronuclear Single Atom Catalysts on Electrochemical Oxygen Reduction

Inter-site interactions between single atom catalysts (SACs) in the high loading regime are critical to tuning the catalytic performance. However, the understanding on such interactions and their distance dependent effects remains elusive, especially for the heteronuclear SACs. In this study, we reveal the effects of the distance-dependent inter-site interaction on the catalytic performance of SACs. Using the density functional theory calculations, we systematically investigate the heteronuclear iron and cobalt single atoms co-supported on the nitrogen-doped graphene (FeN4-C and CoN4-C) for oxygen reduction reaction (ORR). We find that as the distance between Fe and Co SACs decreases, FeN4-C exhibits a reduced catalytic activity, which can be mitigated by the presence of an axial hydroxyl ligand, whereas the activity of CoN4-C shows a volcano-like evolution with the optimum reached at the intermediate distance. We further unravel that the transition towards the high-spin state upon adsorption of ORR intermediate adsorbates is responsible for the decreased activity of both FeN4-C and CoN4-C at short inter-site distance. Such high-spin state transition is also found to significantly shift the linear relation between hydroxyl (*OH) and hydroperoxyl (*OOH) adsorbates. These findings not only shed light on the SAC-specific effect of the distance-dependent inter-site interaction between heteronuclear SACs, but also pave a way towards shifting the long-standing linear relations observed in multiple-electron chemical reactions.

physics.chem-ph↗

arXiv2022

SPICE: Semantic Pseudo-labeling for Image Clustering

The similarity among samples and the discrepancy between clusters are two crucial aspects of image clustering. However, current deep clustering methods suffer from the inaccurate estimation of either feature similarity or semantic discrepancy. In this paper, we present a Semantic Pseudo-labeling-based Image ClustEring (SPICE) framework, which divides the clustering network into a feature model for measuring the instance-level similarity and a clustering head for identifying the cluster-level discrepancy. We design two semantics-aware pseudo-labeling algorithms, prototype pseudo-labeling, and reliable pseudo-labeling, which enable accurate and reliable self-supervision over clustering. Without using any ground-truth label, we optimize the clustering network in three stages: 1) train the feature model through contrastive learning to measure the instance similarity, 2) train the clustering head with the prototype pseudo-labeling algorithm to identify cluster semantics, and 3) jointly train the feature model and clustering head with the reliable pseudo-labeling algorithm to improve the clustering performance. Extensive experimental results demonstrate that SPICE achieves significant improvements (~10%) over existing methods and establishes the new state-of-the-art clustering results on six image benchmark datasets in terms of three popular metrics. Importantly, SPICE significantly reduces the gap between unsupervised and fully-supervised classification; e.g., there is only a 2% (91.8% vs 93.8%) accuracy difference on CIFAR-10. Our code has been made publically available at https://github.com/niuchuangnn/SPICE.

cs.CV↗