Ming Huang

Li Yan, Pengcheng Wei, Hong Xie et al.

Registration is a basic yet crucial task in point cloud processing. In correspondence-based point cloud registration, matching correspondences by point feature techniques may lead to an extremely high outlier ratio. Current methods still suffer from low efficiency, accuracy, and recall rate. We use a simple and intuitive method to describe the 6-DOF (degree of freedom) curtailment process in point cloud registration and propose an outlier removal strategy based on the reliability of the correspondence graph. The method constructs the corresponding graph according to the given correspondences and designs the concept of the reliability degree of the graph node for optimal candidate selection and the reliability degree of the graph edge to obtain the global maximum consensus set. The presented method could achieve fast and accurate outliers removal along with gradual aligning parameters estimation. Extensive experiments on simulations and challenging real-world datasets demonstrate that the proposed method can still perform effective point cloud registration even the correspondence outlier ratio is over 99%, and the efficiency is better than the state-of-the-art. Code is available at https://github.com/WPC-WHU/GROR.

Zheng Chen, Ziwei Yang, Lingwei Zhu et al.

This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance. The framework consists of two parts: the first part extracts informative features by partitioning the input EEG spectrograms into a sequence of time-frequency patches. The second part is constituted by an attention-based architecture to efficiently search for the correlation between partitioned time-frequency patches and defining factors of sleep stages in parallel. The proposed pipeline is validated on the Sleep Heart Health Study dataset with new state-of-the-art results for the stages wake, N2, and N3, obtaining respective F1 scores of 0.93, 0.88, and 0.87, with only EEG signals used. The proposed method also has a high inter-rater reliability of 0.80 kappa. We also visualize the correspondence between sleep staging decisions and features extracted by the proposed method, providing strong interpretability for our model.

Ziwei Yang, Lingwei Zhu, Zheng Chen et al.

Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and classification of cancer subtypes are indispensable for effective clinical treatment. Promising results on automatic cancer subtyping systems have been published recently with the emergence of various deep learning methods. However, such automatic systems often overfit the data due to the high dimensionality and scarcity. In this paper, we propose to investigate automatic subtyping from an unsupervised learning perspective by directly constructing the underlying data distribution itself, hence sufficient data can be generated to alleviate the issue of overfitting. Specifically, we bypass the strong Gaussianity assumption that typically exists but fails in the unsupervised learning subtyping literature due to small-sized samples by vector quantization. Our proposed method better captures the latent space features and models the cancer subtype manifestation on a molecular basis, as demonstrated by the extensive experimental results.

Chen Qi, Yang Jingjing, Huang Ming et al.

The radio map, serving as a visual representation of electromagnetic spatial characteristics, plays a pivotal role in assessment of wireless communication networks and radio monitoring coverage. Addressing the issue of low accuracy existing in the current radio map construction, this paper presents a novel radio map construction method based on generative adversarial network (GAN) in which the Aggregated Contextual-Transformation (AOT) block, Convolutional Block Attention Module (CBAM), and Transposed Convolution (T-Conv) block are applied to the generator, and we name it as ACT-GAN. It significantly improves the reconstruction accuracy and local texture of the radio maps. The performance of ACT-GAN across three different scenarios is demonstrated. Experiment results reveal that in the scenario without sparse discrete observations, the proposed method reduces the root mean square error (RMSE) by 14.6% in comparison to the state-of-the-art models. In the scenario with sparse discrete observations, the RMSE is diminished by 13.2%. Furthermore, the predictive results of the proposed model show a more lucid representation of electromagnetic spatial field distribution. To verify the universality of this model in radio map construction tasks, the scenario of unknown radio emission source is investigated. The results indicate that the proposed model is robust radio map construction and accurate in predicting the location of the emission source.