Guoping Xu

Wuhan Institute of Technology · School of Computer Science and Engineering

While patient-specific seizure prediction deep learning (DL) models can deliver remarkable performance tailored to individual patients, the development of patient-independent models that offer satisfactory cross-subject performance holds greater significance and practicality. However, these patient-independent models, which leverage electroencephal...

Accurately segmenting lesions in ultrasound images is challenging due to the difficulty in distinguishing boundaries between lesions and surrounding tissues. While deep learning has improved segmentation accuracy, there is limited focus on boundary quality and its relationship with body structures. To address this, we introduce UBBS-Net, a dual-bra...

The escalating significance of information security has underscored the per-vasive role of encryption technology in safeguarding communication con-tent. Morse code, a well-established and effective encryption method, has found widespread application in telegraph communication and various do-mains. However, the transmission of Morse code images face...

Background The method of semi‐supervised semantic segmentation entails training with a limited number of labeled samples alongside many unlabeled samples, aiming to reduce dependence on pixel‐level annotations. Most semi‐supervised semantic segmentation methods primarily focus on sample augmentation in spatial dimensions to reduce the shortage of l...

Deep learning has made significant progress in computer vision, specifically in image classification, object detection, and semantic segmentation. The skip connection has played an essential role in the architecture of deep neural networks, enabling easier optimization through residual learning during the training stage and improving accuracy durin...

Purpose: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leadin...

Semantic segmentation is a fundamental step in image understanding, playing a crucial role in the fields of automatic driving, medical image analysis, defect detection, etc. Despite significant progress in deep learning-based image segmentation, challenges in terms of accuracy and efficiency still exist, especially for small-scale objects. In this...

Privacy protection has become increasingly crucial in the field of epilepsy prediction. Some latest studies introduced the source free domain adaptation (SFDA), which only utilizes a pre-trained source model for protecting the source data privacy. However, the existing SFDA methods exist two shortcomings. (1) the offline setting, which is not suita...

Up-sampling operations are frequently utilized to recover the spatial resolution of feature maps in neural networks for segmentation task. However, current upsampling methods, such as bilinear interp olation or deconvolution, do not fully consider the relationship of feature maps, which have negative impact on learning discriminative features for s...

Medical image segmentation plays an essential role in developing computer-assisted diagnosis and treatment systems, yet it still faces numerous challenges. In the past few years, Convolutional Neural Networks (CNNs) have been successfully applied to the task of medical image segmentation. Regrettably, due to the locality of convolution operations,...

Convolutional Neural Networks (CNNs) have been widely used in medical image segmentation to effiiently develop computer-aided diagnosis systems. Due to the locality of convolutional operations, they can be used to extract fie-grained features, but with limitations in building global context and long-range spatial relationships. Recently, shifted wi...

Background: Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of brea...

Deep learning (DL) methods have been widely utilized in ultrasound (US) image segmentation tasks. However, current DL segmentation methods for US images are typically developed only for lesion segmentation of specific organs; e.g., breast or thyroid US. So far, there is currently no general-purpose lesion segmentation framework for US images that c...

Downsampling operations such as max pooling or strided convolution are ubiquitously utilized in Convolutional Neural Networks (CNNs) to aggregate local features, enlarge receptive field, and minimize computational overhead. However, for a semantic segmentation task, pooling features over the local neighbourhood may result in the loss of important s...

Background Non-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest s...

In this paper, we propose an end-to-end epilepsy seizure prediction method based on multi-layer perceptrons (MLPs). The proposed method mainly contains two functional blocks: the denoising-weighted block and the MLPs block. The denoising-weighted block consists of a denoising layer removing some undesired artifacts, a weighted layer which assigns d...

Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems, yet still faces many challenges. Building on convolutional neural networks (CNNs), medical image segmentation has achieved tremendous progress. However, owing to the locality of convolution operations, CNNs have the inherent limitation in learning global cont...

Incidence of primary thyroid cancer rises steadily over the past decades because of overdiagnosis and overtreatment through the improvement in imaging techniques for screening, especially in ultrasound examination. Metastatic status of lymph nodes is important for staging the type of primary thyroid cancer. Deep learning algorithms based on ultraso...

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification of subject-specific brain regions for TMS is impo...

Medical image segmentation plays an essential role in developing computer-assisted diagnosis and therapy systems, yet still faces many challenges. In the past few years, the popular encoder-decoder architectures based on CNNs (e.g., U-Net) have been successfully applied in the task of medical image segmentation. However, due to the locality of conv...

Multi-domain sentiment classification is a challenging topic in natural language processing, where data from multiple domains are applied to improve the performance of classification. Recently, it has been demonstrated that attention neural networks exhibit powerful performance in this task. In the present study, we propose a collaborative attentio...

Purpose Automated lymph node (LN) recognition and segmentation from cross-sectional medical images is an important step for the automated diagnostic assessment of patients with cancer. Yet, it is still a difficult task owing to the low contrast of LNs and surrounding soft tissues as well as due to the variation in nodal size and shape. In this pape...

Purpose The derivation of quantitative information from medical images in a practical manner is essential for quantitative radiology (QR) to become a clinical reality, but still faces a major hurdle because of image segmentation challenges. With the goal of performing disease quantification in lymph node (LN) stations without explicit nodal delinea...

In this study, the authors propose a multi‐task learning with deconvolution network (MTL‐DN) method for the multi‐label classification of multiple power quality disturbances (MPQDs). First, the labels of MPQDs are assigned to three groups corresponding to three learning tasks and the label correlations among various PQDs are utilised in the joint l...

The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition-delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucia...

Currently, there are many papers that have been published on the detection and segmentation of lymph nodes from medical images. However, it is still a challenging problem owing to low contrast with surrounding soft tissues and the variations of lymph node size and shape on computed tomography (CT) images. This is particularly very difficult on low-...