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Dong Wei

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18 papers

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EAAI Journal 2026 Journal Article

A high-precision and efficient method for coal–rock characteristic identification utilizing coal wall temperature field

Futao Li
Zhongbin Wang
Dong Wei
Xin Li
Lei Si
Jinheng Gu
Jialiang Dai

Coal-rock characteristic identification is a crucial technology for realizing shearers intelligent control. To enhance the intelligence level of shearers, this paper presents a novel approach for identifying coal–rock characteristics using the temperature field of the coal wall. First, we introduce an enhanced You Only Look Once (YOLO) model, termed Temperature sensitive region-YOLO (TSR-YOLO), specifically designed to extract temperature-sensitive regions within the coal wall temperature field. In terms of structural design, TSR-YOLO innovatively incorporates the Cross Stage Partial FasterNet (C3k2-FasterNet) into the backbone network to accelerate feature extraction and devises the Cross-Stage Partial Kolmogorov–Arnold Network (C3k2-KAN) to enhance detailed feature representation. In the bottleneck network, it integrates the Dynamic Convolution (DynamicConv) module to capture broader and more complex feature, as well as the Variational Overlapping Vision-Generalized Spatial Cross-Stage Partial (VoV-GSCSP) module to enhance computational efficiency and optimize feature extraction performance. Subsequently, we propose a coal–rock characteristic identification method utilizing the ConvNeXt model. To validate its effectiveness, we conduct ablation and comparative experiments using experimental data obtained from infrared images of the coal wall during the shearer cutting process. The results indicate that proposed approach achieves a mean Average Precision (mAP) reaching 98. 1% and an inference speed of 2 ms per image in identifying temperature-sensitive regions of the coal wall temperature field, surpassing other comparative models. Furthermore, the accuracy of coal–rock property identification reaches 97. 6%. This study presents a new approach to coal–rock characteristic identification methods.

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AAAI Conference 2026 Conference Paper

HyperSign: Saliency-Aware Spatial Graphs and Temporal Hypergraphs for Continuous Sign Language Recognition

Weiyi Ye
Xu-Hua Yang
Dong Wei
Gang-Feng Ma
Yujiao Huang
Xiao-Xin Li

Continuous sign language recognition (CSLR) technology enables social communication for the hearing-impaired by converting sign language videos into text. However, due to the limited receptive fields of convolutional networks and inefficient long-range dependency modeling in temporal modules, current methods find it difficult to capture cross-regional and high-order dynamic semantics in complex gestures. To address these limitations, we propose a dynamic spatiotemporal hypergraph network named HyperSign, which optimizes feature learning through innovative graph architectures. For single-frame spatial modeling, we propose a saliency-aware spatial graph construction strategy that dynamically quantifies semantic saliency by integrating feature complexity and motion intensity information from patches. This strategy can adaptively adjust node connectivity based on the computed saliency, thereby enabling the graph structure to focus on information-dense regions such as hands and faces. For temporal dependency modeling, we abandon the conventional pairwise frame interactions and propose a temporal hypergraph construction method. This method employs a learnable clustering algorithm to aggregate semantically correlated nodes within temporal windows into hyperedges, thereby explicitly capturing high-order associations within individual gesture actions that span multiple frames. Extensive experiments on the PHOENIX14, PHOENIX14-T, and CSL-Daily datasets demonstrate that HyperSign outperforms the state-of-the-art (SOTA) approaches in CSLR without any additional annotation information, establishing a new feature learning paradigm for the CSLR task.

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EAAI Journal 2025 Journal Article

A small sample crop seed quality detection system based on spectral-image bimodal fusion technology

Qixing Tang
Longbao Liu
Dong Wei
Juan Liao
Zhongliang Liu
Leizi Jiao
Yachao Liu
Ke Wang

The quality of crop seeds directly affects agricultural productivity and yield, making reliable quality assessments. However, traditional detection methods are often time-consuming and heavily reliant on operator expertise. To address these challenges, this study proposed a crop seed quality detection system that integrates near-infrared (NIR) spectral data with red, green, blue (RGB) image data. Using corn seeds as a case study with a limited sample size, we developed a Multi-Stage Attention-FuseNet (MSA-FuseNet). In the first stage, NIR and RGB data were fused through direct concatenation, followed by feature extraction using a Channel Attention Module (CAM) and a Spatial Attention Module (SAM). The second stage focused on RGB image data processing and feature extraction. Finally, the outputs from both stages were integrated through summation. The experimental results showed that MSA-FuseNet achieved 96. 92 % accuracy while requiring only minimal sample sizes and computational resources, demonstrating its efficiency and precision in crop seed quality detection with limited data. This study provides an important reference for efficient sorting of crop seeds at the single-seed level.

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NeurIPS Conference 2025 Conference Paper

D-VST: Diffusion Transformer for Pathology-Correct Tone-Controllable Cross-Dye Virtual Staining of Whole Slide Images

Shurong Yang
Dong Wei
Yihuang Hu
Qiong Peng
Hong Liu
Yawen Huang
Xian Wu
Yefeng Zheng

Diffusion-based virtual staining methods of histopathology images have demonstrated outstanding potential for stain normalization and cross-dye staining (e. g. , hematoxylin-eosin to immunohistochemistry). However, achieving pathology-correct cross-dye virtual staining with versatile tone controls poses significant challenges due to the difficulty of decoupling the given pathology and tone conditions. This issue would cause non-pathologic regions to be mistakenly stained like pathologic ones, and vice versa, which we term “pathology leakage. ” To address this issue, we propose diffusion virtual staining Transformer (D-VST), a new framework with versatile tone control for cross-dye virtual staining. Specifically, we introduce a pathology encoder in conjunction with a tone encoder, combined with a two-stage curriculum learning scheme that decouples pathology and tone conditions, to enable tone control while eliminating pathology leakage. Further, to extend our method for billion-pixel whole slide image (WSI) staining, we introduce a novel frequency-aware adaptive patch sampling strategy for high-quality yet efficient inference of ultra-high resolution images in a zero-shot manner. Integrating these two innovative components facilitates a pathology-correct, tone-controllable, cross-dye WSI virtual staining process. Extensive experiments on three virtual staining tasks that involve translating between four different dyes demonstrate the superiority of our approach in generating high-quality and pathologically accurate images compared to existing methods based on generative adversarial networks and diffusion models. Our code and trained models will be released.

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EAAI Journal 2025 Journal Article

Nonlinear dynamic modeling of turbojet engines using combined convolutional and long short-term memory networks

Chen Lei
Dong Wei
Su Hang
Chi Yutian
Tian Congling
Gao Yongzhuo
Wu Dongmei
Dong Hui

Turbojet engines are widely used in small-scale aerial vehicles, but their nonlinear and time-varying dynamics present significant challenges for accurate modeling and control. Traditional system identification methods often struggle to capture these complex behaviors, particularly under limited data conditions. This study proposes a novel hybrid neural network architecture that combines convolutional neural networks and long short-term memory units. The model is specifically designed for small-sample scenarios, enabling robust learning and precise engine speed prediction from real input-output sequences. The input vector comprises the current engine speed, the next-step pulse-width modulation command, and its increment, enhancing the model’s responsiveness and reducing phase-lag effects. The proposed model is trained and evaluated on a real-world dataset containing 38, 257 samples, with 80 % used for training and 20 % for testing. Its predictive performance is assessed using step input responses and three evaluation metrics: mean absolute error, root mean square error, and Pearson correlation coefficient. Experimental results demonstrate that the proposed hybrid architecture outperforms other recurrent models in capturing transient dynamics and accurately reproducing real engine behavior. These findings highlight the model’s effectiveness in modeling nonlinear engine dynamics and its potential as a data-efficient alternative to traditional identification techniques for small-scale turbojet applications.

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EAAI Journal 2025 Journal Article

Optimization of centrifugal pump performance and excitation force based on machine learning and enhanced non-dominated sorting genetic algorithm III

Jiang Haoqing
Dong Wei
Li Sucheng
He Fan

This study addresses the issue of excessive vibration in centrifugal pumps without compromising their performance by employing six machine learning methods. These include the Backpropagation (BP) Neural Network, Feedforward Neural Network (FNN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), and Tree Ensemble (TE). These methods are used to construct a mapping model that correlates optimization objectives and parameters. The model served as the fitness function in the Adaptive Non-dominated Sorting Genetic Algorithm III (A-NSGA-III). A total of 1000 optimization results generated by the A-NSGA-III were evaluated to determine the final optimization scheme, using a reverse design approach to identify optimal parameters. A comparison of the hydraulic performance before and after optimization revealed minor improvements in the head, efficiency, and shaft power. The radial force decreased by 17. 81%, and the axial force decreased by a substantial 98. 12%, meeting the set objectives. The significant reduction in axial force was predominantly observed at the balance chamber. Entropy production theory analysis indicated a considerable decrease in the entropy production rate on the impeller's inner wall. Cross-wavelet transform and wavelet coherence analysis showed a significant correlation between the entropy production rate and the energy variation of the axial force. Before optimization, there was a pronounced coherence between the entropy production rate and the resonance period, which was notably reduced after optimization. This method significantly addresses the issue of excessive axial force in the operation of centrifugal pumps without compromising the basic hydraulic performance. This study provides a reference for the structural optimization of centrifugal pumps.

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NeurIPS Conference 2024 Conference Paper

Continuous Heatmap Regression for Pose Estimation via Implicit Neural Representation

Shengxiang Hu
Huaijiang Sun
Dong Wei
Xiaoning Sun
Jin Wang

Heatmap regression has dominated human pose estimation due to its superior performance and strong generalization. To meet the requirements of traditional explicit neural networks for output form, existing heatmap-based methods discretize the originally continuous heatmap representation into 2D pixel arrays, which leads to performance degradation due to the introduction of quantization errors. This problem is significantly exacerbated as the size of the input image decreases, which makes heatmap-based methods not much better than coordinate regression on low-resolution images. In this paper, we propose a novel neural representation for human pose estimation called NerPE to achieve continuous heatmap regression. Given any position within the image range, NerPE regresses the corresponding confidence scores for body joints according to the surrounding image features, which guarantees continuity in space and confidence during training. Thanks to the decoupling from spatial resolution, NerPE can output the predicted heatmaps at arbitrary resolution during inference without retraining, which easily achieves sub-pixel localization precision. To reduce the computational cost, we design progressive coordinate decoding to cooperate with continuous heatmap regression, in which localization no longer requires the complete generation of high-resolution heatmaps. The code is available at https: //github. com/hushengxiang/NerPE.

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AAAI Conference 2024 Conference Paper

Enhanced Fine-Grained Motion Diffusion for Text-Driven Human Motion Synthesis

Dong Wei
Xiaoning Sun
Huaijiang Sun
Shengxiang Hu
Bin Li
Weiqing Li
Jianfeng Lu

The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently. However, in most cases, textual expressions only contain general and qualitative motion descriptions, while lack fine depiction and sufficient intensity, leading to the synthesized motions that either (a) semantically compliant but uncontrollable over specific pose details, or (b) even deviates from the provided descriptions, bringing animators with undesired cases. In this paper, we propose DiffKFC, a conditional diffusion model for text-driven motion synthesis with KeyFrames Collaborated, enabling realistic generation with collaborative and efficient dual-level control: coarse guidance at semantic level, with only few keyframes for direct and fine-grained depiction down to body posture level. Unlike existing inference-editing diffusion models that incorporate conditions without training, our conditional diffusion model is explicitly trained and can fully exploit correlations among texts, keyframes and the diffused target frames. To preserve the control capability of discrete and sparse keyframes, we customize dilated mask attention modules where only partial valid tokens participate in local-to-global attention, indicated by the dilated keyframe mask. Additionally, we develop a simple yet effective smoothness prior, which steers the generated frames towards seamless keyframe transitions at inference. Extensive experiments show that our model not only achieves state-of-the-art performance in terms of semantic fidelity, but more importantly, is able to satisfy animator requirements through fine-grained guidance without tedious labor.

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AAAI Conference 2024 Conference Paper

Federated Modality-Specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation

Qian Dai
Dong Wei
Hong Liu
Jinghan Sun
Liansheng Wang
Yefeng Zheng

Most existing federated learning (FL) methods for medical image analysis only considered intramodal heterogeneity, limiting their applicability to multimodal imaging applications. In practice, it is not uncommon that some FL participants only possess a subset of the complete imaging modalities, posing inter-modal heterogeneity as a challenge to effectively training a global model on all participants’ data. In addition, each participant would expect to obtain a personalized model tailored for its local data characteristics from the FL in such a scenario. In this work, we propose a new FL framework with federated modality-specific encoders and multimodal anchors (FedMEMA) to simultaneously address the two concurrent issues. Above all, FedMEMA employs an exclusive encoder for each modality to account for the inter-modal heterogeneity in the first place. In the meantime, while the encoders are shared by the participants, the decoders are personalized to meet individual needs. Specifically, a server with full-modal data employs a fusion decoder to aggregate and fuse representations from all modality-specific encoders, thus bridging the modalities to optimize the encoders via backpropagation reversely. Meanwhile, multiple anchors are extracted from the fused multimodal representations and distributed to the clients in addition to the encoder parameters. On the other end, the clients with incomplete modalities calibrate their missing-modal representations toward the global full-modal anchors via scaled dot-product cross-attention, making up the information loss due to absent modalities while adapting the representations of present ones. FedMEMA is validated on the BraTS 2020 benchmark for multimodal brain tumor segmentation. Results show that it outperforms various up-to-date methods for multimodal and personalized FL and that its novel designs are effective. Our code is available.

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AIIM Journal 2024 Journal Article

Triplet-branch network with contrastive prior-knowledge embedding for disease grading

Yuexiang Li
Yanping Wang
Guang Lin
Yawen Huang
Jingxin Liu
Yi Lin
Dong Wei
Qirui Zhang

Since different disease grades require different treatments from physicians, i. e. , the low-grade patients may recover with follow-up observations whereas the high-grade may need immediate surgery, the accuracy of disease grading is pivotal in clinical practice. In this paper, we propose a Triplet-Branch Network with ContRastive priOr-knoWledge embeddiNg (TBN-CROWN) for the accurate disease grading, which enables physicians to accordingly take appropriate treatments. Specifically, our TBN-CROWN has three branches, which are implemented for representation learning, classifier learning and grade-related prior-knowledge learning, respectively. The former two branches deal with the issue of class-imbalanced training samples, while the latter one embeds the grade-related prior-knowledge via a novel auxiliary module, termed contrastive embedding module. The proposed auxiliary module takes the features embedded by different branches as input, and accordingly constructs positive and negative embeddings for the model to deploy grade-related prior-knowledge via contrastive learning. Extensive experiments on our private and two publicly available disease grading datasets show that our TBN-CROWN can effectively tackle the class-imbalance problem and yield a satisfactory grading accuracy for various diseases, such as fatigue fracture, ulcerative colitis, and diabetic retinopathy.

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AAAI Conference 2023 Conference Paper

Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction

Dong Wei
Huaijiang Sun
Bin Li
Jianfeng Lu
Weiqing Li
Xiaoning Sun
Shengxiang Hu

Stochastic human motion prediction aims to forecast multiple plausible future motions given a single pose sequence from the past. Most previous works focus on designing elaborate losses to improve the accuracy, while the diversity is typically characterized by randomly sampling a set of latent variables from the latent prior, which is then decoded into possible motions. This joint training of sampling and decoding, however, suffers from posterior collapse as the learned latent variables tend to be ignored by a strong decoder, leading to limited diversity. Alternatively, inspired by the diffusion process in nonequilibrium thermodynamics, we propose MotionDiff, a diffusion probabilistic model to treat the kinematics of human joints as heated particles, which will diffuse from original states to a noise distribution. This process not only offers a natural way to obtain the "whitened'' latents without any trainable parameters, but also introduces a new noise in each diffusion step, both of which facilitate more diverse motions. Human motion prediction is then regarded as the reverse diffusion process that converts the noise distribution into realistic future motions conditioned on the observed sequence. Specifically, MotionDiff consists of two parts: a spatial-temporal transformer-based diffusion network to generate diverse yet plausible motions, and a flexible refinement network to further enable geometric losses and align with the ground truth. Experimental results on two datasets demonstrate that our model yields the competitive performance in terms of both diversity and accuracy.

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AAAI Conference 2023 Conference Paper

M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities

Hong Liu
Dong Wei
Donghuan Lu
Jinghan Sun
Liansheng Wang
Yefeng Zheng

Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-region analysis of brain tumors. Plenty of methods have been proposed for automatic brain tumor segmentation using four common MRI modalities and achieved remarkable performance. In practice, however, it is common to have one or more modalities missing due to image corruption, artifacts, acquisition protocols, allergy to contrast agents, or simply cost. In this work, we propose a novel two-stage framework for brain tumor segmentation with missing modalities. In the first stage, a multimodal masked autoencoder (M3AE) is proposed, where both random modalities (i.e., modality dropout) and random patches of the remaining modalities are masked for a reconstruction task, for self-supervised learning of robust multimodal representations against missing modalities. To this end, we name our framework M3AE. Meanwhile, we employ model inversion to optimize a representative full-modal image at marginal extra cost, which will be used to substitute for the missing modalities and boost performance during inference. Then in the second stage, a memory-efficient self distillation is proposed to distill knowledge between heterogenous missing-modal situations while fine-tuning the model for supervised segmentation. Our M3AE belongs to the ‘catch-all’ genre where a single model can be applied to all possible subsets of modalities, thus is economic for both training and deployment. Extensive experiments on BraTS 2018 and 2020 datasets demonstrate its superior performance to existing state-of-the-art methods with missing modalities, as well as the efficacy of its components. Our code is available at: https://github.com/ccarliu/m3ae.

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AAAI Conference 2022 Conference Paper

Boost Supervised Pretraining for Visual Transfer Learning: Implications of Self-Supervised Contrastive Representation Learning

Jinghan Sun
Dong Wei
Kai Ma
Liansheng Wang
Yefeng Zheng

Unsupervised pretraining based on contrastive learning has made significant progress recently and showed comparable or even superior transfer learning performance to traditional supervised pretraining on various tasks. In this work, we first empirically investigate when and why unsupervised pretraining surpasses supervised counterparts for image classification tasks with a series of control experiments. Besides the commonly used accuracy, we further analyze the results qualitatively with the class activation maps and assess the learned representations quantitatively with the representation entropy and uniformity. Our core finding is that it is the amount of information effectively perceived by the learning model that is crucial to transfer learning, instead of absolute size of the dataset. Based on this finding, we propose Classification Activation Map guided contrastive (CAMtrast) learning which better utilizes the label supervision to strengthen supervised pretraining, by making the networks perceive more information from the training images. CAMtrast is evaluated with three fundamental visual learning tasks: image recognition, object detection, and semantic segmentation, on various public datasets. Experimental results show that our CAMtrast effectively improves the performance of supervised pretraining, and that its performance is superior to both unsupervised counterparts and a recent related work which similarly attempted improving supervised pretraining.

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JBHI Journal 2022 Journal Article

Mix-and-Interpolate: A Training Strategy to Deal With Source-Biased Medical Data

Yuexiang Li
Jiawei Chen
Dong Wei
Yanchun Zhu
Jianrong Wu
Junfeng Xiong
Yadong Gang
Wenbo Sun

Till March 31st, 2021, the coronavirus disease 2019 (COVID-19) had reportedly infected more than 127 million people and caused over 2. 5 million deaths worldwide. Timely diagnosis of COVID-19 is crucial for management of individual patients as well as containment of the highly contagious disease. Having realized the clinical value of non-contrast chest computed tomography (CT) for diagnosis of COVID-19, deep learning (DL) based automated methods have been proposed to aid the radiologists in reading the huge quantities of CT exams as a result of the pandemic. In this work, we address an overlooked problem for training deep convolutional neural networks for COVID-19 classification using real-world multi-source data, namely, the data source bias problem. The data source bias problem refers to the situation in which certain sources of data comprise only a single class of data, and training with such source-biased data may make the DL models learn to distinguish data sources instead of COVID-19. To overcome this problem, we propose MIx-aNd-Interpolate (MINI), a conceptually simple, easy-to-implement, efficient yet effective training strategy. The proposed MINI approach generates volumes of the absent class by combining the samples collected from different hospitals, which enlarges the sample space of the original source-biased dataset. Experimental results on a large collection of real patient data (1, 221 COVID-19 and 1, 520 negative CT images, and the latter consisting of 786 community acquired pneumonia and 734 non-pneumonia) from eight hospitals and health institutions show that: 1) MINI can improve COVID-19 classification performance upon the baseline (which does not deal with the source bias), and 2) MINI is superior to competing methods in terms of the extent of improvement.

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AAAI Conference 2021 Conference Paper

Alternative Baselines for Low-Shot 3D Medical Image Segmentation—An Atlas Perspective

Shuxin Wang
Shilei Cao
Dong Wei
Cong Xie
Kai Ma
Liansheng Wang
Deyu Meng
Yefeng Zheng

Low-shot (one/few-shot) segmentation has attracted increasing attention as it works well with limited annotation. Stateof-the-art low-shot segmentation methods on natural images usually focus on implicit representation learning for each novel class, such as learning prototypes, deriving guidance features via masked average pooling, and segmenting using cosine similarity in feature space. We argue that low-shot segmentation on medical images should step further to explicitly learn dense correspondences between images to utilize the anatomical similarity. The core ideas are inspired by the classical practice of multi-atlas segmentation, where the indispensable parts of atlas-based segmentation, i. e. , registration, label propagation, and label fusion are unified into a single framework in our work. Specifically, we propose two alternative baselines, i. e. , the Siamese-Baseline and Individual- Difference-Aware Baseline, where the former is targeted at anatomically stable structures (such as brain tissues), and the latter possesses a strong generalization ability to organs suffering large morphological variations (such as abdominal organs). In summary, this work sets up a benchmark for lowshot 3D medical image segmentation and sheds light on further understanding of atlas-based few-shot segmentation.

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EAAI Journal 2021 Journal Article

An image segmentation method based on a modified local-information weighted intuitionistic Fuzzy C-means clustering and Gold-panning Algorithm

Dong Wei
Zhongbin Wang
Lei Si
Chao Tan
Xuliang Lu

The image segmentation method based on clustering analysis has the advantages of small sample space constraints and strong universality. As an unsupervised clustering algorithm, the fuzzy C-means clustering algorithm is widely used in practical engineering. However, it is still some shortcomings: the fuzzy C-means clustering algorithm is difficult to interpret the noise effectively, which makes it more sensitive to the noise, and the selection of key parameters has to be made by trial and error experiments, reducing the adaptability of the algorithm. Besides, its iteration process is heavily influenced by the initial clustering centers and easy to fall into local optimum. Therefore, an intuitionistic Fuzzy C-means clustering method, based on local-information weight, is proposed in this paper. By introducing the local-information weight, the proposed algorithm adjusts the local-information influence weight adaptively in fuzzy partition, which enhances its robustness to noisy images. Furthermore, a novel swarm intelligence algorithm, called the Gold-Panning Algorithm, is proposed to optimize the initial clustering centers and key parameters in the clustering algorithm. By utilizing the Gold-Panning Algorithm, the adaptability of the proposed clustering algorithm is further improved. In this paper, the proposed methods are explained in detail and compared with the existing methods to demonstrate its superior performance.

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AAAI Conference 2021 Conference Paper

Multiple Kernel Clustering with Kernel k-Means Coupled Graph Tensor Learning

Zhenwen Ren
Quansen Sun
Dong Wei

Kernel k-means (KKM) and spectral clustering (SC) are two basic methods used for multiple kernel clustering (MKC), which have both been widely used to identify clusters that are non-linearly separable. However, both of them have their own shortcomings: 1) the KKM-based methods usually focus on learning a discrete clustering indicator matrix via a combined consensus kernel, but cannot exploit the high-order affinities of all pre-defined base kernels; and 2) the SC-based methods require a robust and meaningful affinity graph in kernel space as input in order to form clusters with desired clustering structure. In this paper, a novel method, kernel k-means coupled graph tensor (KCGT), is proposed to graciously couple KKM and SC for seizing their merits and evading their demerits simultaneously. In specific, we innovatively develop a new graph learning paradigm by leveraging an explicit theoretical connection between clustering indicator matrix and affinity graph, such that the affinity graph propagated from KKM enjoys the valuable block diagonal and sparse property. Then, by using this graph learning paradigm, base kernels can produce multiple candidate affinity graphs, which are stacked into a low-rank graph tensor for capturing the highorder affinity of all these graphs. After that, by averaging all the frontal slices of the tensor, a high-quality affinity graph is obtained. Extensive experiments have shown the superiority of KCGT compared with the state-of-the-art MKC methods.

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JBHI Journal 2020 Journal Article

Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank

Yuexiang Li
Dong Wei
Jiawei Chen
Shilei Cao
Hongyu Zhou
Yanchun Zhu
Jianrong Wu
Lan Lan

Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremendous pressure on radiologists for reading the exams and may potentially lead to fatigue-related mis-diagnosis. Reliable automatic classification algorithms can be really helpful; however, they usually require a considerable number of COVID-19 cases for training, which is difficult to acquire in a timely manner. Meanwhile, how to effectively utilize the existing archive of non-COVID-19 data (the negative samples) in the presence of severe class imbalance is another challenge. In addition, the sudden disease outbreak necessitates fast algorithm development. In this work, we propose a novel approach for effective and efficient training of COVID-19 classification networks using a small number of COVID-19 CT exams and an archive of negative samples. Concretely, a novel self-supervised learning method is proposed to extract features from the COVID-19 and negative samples. Then, two kinds of soft-labels (‘difficulty’ and ‘diversity’) are generated for the negative samples by computing the earth mover's distances between the features of the negative and COVID-19 samples, from which data ‘values’ of the negative samples can be assessed. A pre-set number of negative samples are selected accordingly and fed to the neural network for training. Experimental results show that our approach can achieve superior performance using about half of the negative samples, substantially reducing model training time.

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