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2. Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training

Author

Lin, Yi, Qu, Zhiyong, Chen, Hao, Gao, Zhongke, Li, Yuexiang, Xia, Lili, Ma, Kai, Zheng, Yefeng, and Cheng, Kwang-Ting

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. Code: https://github.com/hust-linyi/SC-Net, Comment: Accepted by MedIA

Published
2022
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9. EEG-Based Motor Imagery Recognition Framework via Multisubject Dynamic Transfer and Iterative Self-Training

Author

Wang, He, Chen, Peiyin, Zhang, Meng, Zhang, Jianbo, Sun, Xinlin, Li, Mengyu, Yang, Xiong, and Gao, Zhongke

Abstract

A robust decoding model that can efficiently deal with the subject and period variation is urgently needed to apply the brain–computer interface (BCI) system. The performance of most electroencephalogram (EEG) decoding models depends on the characteristics of specific subjects and periods, which require calibration and training with annotated data prior to application. However, this situation will become unacceptable as it would be difficult for subjects to collect data for an extended period, especially in the rehabilitation process of disability based on motor imagery (MI). To address this issue, we propose an unsupervised domain adaptation framework called iterative self-training multisubject domain adaptation (ISMDA) that focuses on the offline MI task. First, the feature extractor is purposefully designed to map the EEG to a latent space of discriminative representations. Second, the attention module based on dynamic transfer matches the source domain and target domain samples with a higher coincidence degree in latent space. Then, an independent classifier oriented to the target domain is employed in the first stage of the iterative training process to cluster the samples of the target domain through similarity. Finally, a pseudolabel algorithm based on certainty and confidence is employed in the second stage of the iterative training process to adequately calibrate the error between prediction and empirical probabilities. To evaluate the effectiveness of the model, extensive testing has been performed on three publicly available MI datasets, the BCI IV IIa, the High gamma dataset, and Kwon et al. datasets. The proposed method achieved 69.51%, 82.38%, and 90.98% cross-subject classification accuracy on the three datasets, which outperforms the current state-of-the-art offline algorithms. Meanwhile, all results demonstrated that the proposed method could address the main challenges of the offline MI paradigm.

Published
2024
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12. Nuclei segmentation with point annotations from pathology images via self-supervised learning and co-training

Author

Lin, Yi, Qu, Zhiyong, Chen, Hao, Gao, Zhongke, Li, Yuexiang, Xia, Lili, Ma, Kai, Zheng, Yefeng, Cheng, Kwang Ting, Lin, Yi, Qu, Zhiyong, Chen, Hao, Gao, Zhongke, Li, Yuexiang, Xia, Lili, Ma, Kai, Zheng, Yefeng, and Cheng, Kwang Ting

Abstract

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. Codes are available at https://github.com/hust-linyi/SC-Net. © 2023 Elsevier B.V.

Published
2023

19. Multilayer Visibility Graph-Based Ordinal Network for Revealing Gas–Liquid Nonlinear Dynamic Flow Behaviors

Author

Lv, Dongmei, Dang, Weidong, Min, Rui, Wu, Meng, Guo, Wei, Li, Mengyu, Wang, Ruiqi, and Gao, Zhongke

Abstract

Characterizing the nonlinear dynamic behaviors of gas–liquid two-phase flow is a challenging but rewarding research topic. In this article, a novel multilayer visibility graph-based ordinal network (MVGON) is proposed for exploring the gas–liquid flow behaviors. In particular, the vertical upward gas–liquid flow experiments are first conducted in a 50 mm inner-diameter pipe, and multivariate measurement data is acquired. Then, MVGON is inferred from experimental signals. MVGON not only can effectively fuse multivariate time series but also enables to further excavate concise key information based on inheriting system features extracted by visibility graph (VG). For each derived projection network of MVGON, graph energy and average clustering coefficient (ACC) are calculated for quantitatively characterizing the topological structure of MVGON. In addition, permutation entropy (PE) and multivariate pseudo Wigner distribution-based time–frequency distribution are calculated for the three typical flow patterns to support our findings. The results indicate that our MVGON framework allows effectively characterizing the nonlinear dynamic behaviors during the evolution of different gas–liquid flow patterns. Meanwhile, it provides a novel approach for characterizing the complex system dynamics based on multivariate time series analysis.

Published
2024
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20. Flashlight-Net: A Modular Convolutional Neural Network for Motor Imagery EEG Classification

Author

Dang, Weidong, Lv, Dongmei, Tang, Mengxiao, Sun, Xinlin, Liu, Yong, Grebogi, Celso, and Gao, Zhongke

Abstract

Brain-computer interface (BCI) establishes an interactive platform by translating brain activity patterns into commands of external devices. BCIs, especially motor imagery (MI)-based BCIs, have injected new vitality into the development of rehabilitation medicine and many other fields. In this work, one convolutional neural network, named as Flashlight-Net model, is proposed for multiclass MI classification. Flashlight-Net model adopts modular design, in which channel fusion module and time domain module ensure the directions of feature extraction, while feature pool module reduces the loss of effective information. Given the multi-frequency nature of the brain, we combine three frequency bands and construct an ensemble Flashlight-Net model. During the model training, by means of transfer learning, pretraining and fine-tuning processes are designed to integrate training samples from multiple subjects. The experimental results on publicly available BCI Competition IV-2a dataset show that the proposed model can achieve good results on all nine subjects, with an average classification accuracy of 81.23% for four classes. All these demonstrate that the proposed Flashlight-Net model can effectively decode multi-channel and multiclass MI signals.

Published
2024
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22. Label Propagation for Annotation-Efficient Nuclei Segmentation from Pathology Images

Author

Lin, Yi, Qu, Zhiyong, Chen, Hao, Gao, Zhongke, Li, Yuexiang, Xia, Lili, Ma, Kai, Zheng, Yefeng, and Cheng, Kwang-Ting

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), FOS: Biological sciences, Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM)
Abstract

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. The proposed method achieves label propagation in a coarse-to-fine manner as follows. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H\&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. The source codes for implementing the experiments will be released after acceptance.

Published
2022
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50. A Brain Network Analysis-Based Double Way Deep Neural Network for Emotion Recognition.

Author

Ma C, Niu W, Sun X, Li M, and Gao Z

Abstract

Constructing reliable and effective models to recognize human emotional states has become an important issue in recent years. In this article, we propose a double way deep residual neural network combined with brain network analysis, which enables the classification of multiple emotional states. To begin with, we transform the emotional EEG signals into five frequency bands by wavelet transform and construct brain networks by inter-channel correlation coefficients. These brain networks are then fed into a subsequent deep neural network block which contains several modules with residual connection and enhanced by channel attention mechanism and spatial attention mechanism. In the second way of the model, we feed the emotional EEG signals directly into another deep neural network block to extract temporal features. At the end of the two ways, the features are concatenated for classification. To verify the effectiveness of our proposed model, we carried out a series of experiments to collect emotional EEG from eight subjects. The average accuracy of the proposed model on our emotional dataset is 94.57%. In addition, the evaluation results on public databases SEED and SEED-IV are 94.55% and 78.91%, respectively, demonstrating the superiority of our model in emotion recognition tasks.

Published
2023
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