Author name cluster

Baiying Lei

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

21 papers

1 author row

JBHI Journal 2026 Journal Article

HDPL: Hypergraph-based Dynamic Prompting Learning for Incomplete Multimodal Medical Learning

Xiaomin Zhou
Guoheng Huang
Qin Zhao
Jianbin He
Xiaochen Yuan
Ming Li
Chi-Man Pun
Ling Guo

Multimodal learning has garnered significant attention in the medical field due to its ability to provide a more comprehensive perspective utilizing various types of data, that aids in making more accurate decisions. However, the complexity of medical data, coupled with missing modalities, severely hinders predictive accuracy. Existing methods for multimodal learning with missing modalities still face considerable challenges. For instance, approaches that construct multimodal shared feature spaces often result in high computational costs, while methods that infer missing modalities based on complete ones may overly rely on the complete modalities, potentially skewing results. Pre-trained transformer methods address these issues but still have limitations, such as it can only process one missing modality at testing-stage. This is partly because structured data, unlike sequential data, lacks inherent minimum semantic units or natural order. Additionally, the positional encodings generated by this type of methods may introduce information interference when applied to structured data, leading to poor alignment with sequential data during modality fusion in transformer models. To tackle these challenges, we introduce HDPL: Hypergraph-based Dynamic Prompt Learning for Incomplete Multimodal Medical Learning, comprising three modules. The High-Order Hypergraph Embedding module can identify the minimal semantic units within structured data and utilizes hypergraph structures to extract high-dimensional features from clinical data. The Multimodal Medical Data Integrator module closes the distance of the embedding vectors corresponding in the shared space of modality-features, facilitating the integration of modalities in transformer. The Dynamic Network Structure Optimization module is a dynamic learning network by dynamically change the width and depth of network, improving the overall performance of the model, and it alleviates the shortcomings caused by incomplete modality to some extent. Through comprehensive experimentation, we demonstrate the efficiency and robustness of our model in dealing missing modalities and reducing training-burdens. Our code and dataset are available at https://github.com/colorful823/HDPL.

JBHI Journal 2025 Journal Article

An AI-Driven Interpretable Multiview Feature Learning Approach for EEG Based Epileptic Seizure Detection

Ijaz Ahmad
Sarra Ayouni
Faizan Ahmad
Haiying Li
Sunday Timothy Aboyeji
Hazrat Bilal
Inam Ullah
Mohammed Salem Atoum

Epilepsy is a chronic neurological disorder that significantly affects the quality of life (QoL), often causing irreversible brain damage and physical impairment. Electroencephalography (EEG) signal analysis is crucial for monitoring epilepsy, enabling early seizure detection and timely intervention. Effective seizure detection requires the identification of interpretable features from the EEG signal to improve clinical outcomes. This study proposes a novel interpretable multi-view feature learning approach (IMV-FL), in which the time-domain signals and Discrete Fourier Transform (DFT) are applied to convert the time-domain EEG signal into frequency-domain representations. To develop initial multiview feature extraction and compression, spatial and temporal morphological features are extracted from optimal layers of ResNet and Long Short-Term Memory (LSTM) models, with feature compression performed using a Deep Neural Network (DNN). To construct an interpretable multi-view feature fusion, linear and nonlinear properties are calculated for the feature and with fusion strategies. The selected features are processed using the Mutual Information-Based Feature (MIBF) selection algorithm, and a Stacking Ensemble Classifier (SAEC) is adopted for unified view classification. To enhance clinical interpretability, SHapley Additive exPlanations (SHAP) is applied. The proposed framework outperforms single-view feature learning methods by 3% on average and state-of-the-art techniques by 2% in classification accuracy, sensitivity, specificity, and F1-score using the CHB-MIT Scalp and Bonn EEG datasets. This approach offers an effective tool for EEG-based seizure detection (ESD) in clinical and healthcare settings.

JBHI Journal 2025 Journal Article

Dual-Scale Swin Transformer via Feature Alignment and Adversarial Discrimination for Retinopathy of Prematurity Diagnosis

Shaobin Chen
Xinyu Zhao
Yiyao Liu
Hai Xie
Zhenquan Wu
Yingpeng Xie
Yongtao Zhang
Yafeng Li

Retinopathy of prematurity (ROP) is a retinal vascular disease that primarily affects premature infants with low birth weight. It is a leading cause of childhood blindness worldwide, but it can often be effectively managed with appropriate and timely diagnosis and treatment. To address the impact of image style on model classification performance, this paper proposes a dual-scale Swin Transformer (DS-Swin-T) network for ROP. The network comprises three components: image synthesis (IS), feature alignment, and advanced adversarial learning. The IS module generates synthesis style images as an intermediate latent space between source and target styles, reducing style difference. The DS-Swin-T serves as the primary framework for image feature extraction. Detail and style encoders extract features in the shallow feature space, with detail and style losses aligning these features to ensure consistency across styles. To extract rich style-invariant features and ensure consistent classification within the same category, adversarial learning is applied in the advanced feature space. Finally, feature fusion units process dual-scale classification representations. Our method achieves an average accuracy of 97. 91% on the source style dataset. When transferred to other target style datasets, our method effectively mitigates the performance degradation caused by style difference, reaching a maximum average accuracy of 93. 66%. Extensive experiments demonstrate the effectiveness of our method.

JBHI Journal 2025 Journal Article

Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction

Yunlu Yan
Chun-Mei Feng
Yuexiang Li
Ping Li
Rick Siow Mong Goh
Baiying Lei
Weiming Wang
David Dagan Feng

While multi-modal learning has been widely used for MRI reconstruction, it relies on paired multi-modal data, which is difficult to acquire in real clinical scenarios. Especially in the federated setting, there is a common issue that several medical institutions suffer from missing modalities or even only have single-modal data. Therefore, it is infeasible to deploy a standard federated learning framework in such conditions. In this paper, we propose a novel communication-efficient federated learning framework (namely Fed-PMG) to address the missing modality challenge in federated multi-modal MRI reconstruction. Specifically, we utilize a pseudo modality generation mechanism to recover the missing modality for each single-modal client by sharing the distribution information of the amplitude spectrum in frequency space. However, the step of sharing the original amplitude spectrum leads to heavy communication costs. To reduce the communication cost, we introduce a clustering scheme to project the set of amplitude spectrum into a finite number of cluster centroids and share them among the clients. With such an elaborate design, our approach can effectively complete the missing modality within an acceptable communication cost. Extensive experimental results demonstrate that our proposed method can outperform state-of-the-art methods and reach a performance similar to the ideal scenario (i. e. , all clients have the full set of modalities).

EAAI Journal 2025 Journal Article

Label-guided graph learning network via two-stage cross-modal fusion for multi-label skin disease diagnosis

Cheng Zhao
Chunlun Xiao
Feifei Jin
Anqi Zhu
Zhuo Xiang
Yiyao Liu
Lehang Guo
Tianfu Wang

The accurate diagnosis of skin diseases relies on combining cortical lesion morphological features from clinical and dermoscopic data with deep subcutaneous lesion characteristics from ultrasound data. However, current multi-modal diagnostic methods mainly emphasize clinical and dermoscopic data analysis, lacking a thorough exploration of subcutaneous tissue features provided by skin ultrasound. Additionally, existing research often overlooks the analysis of relationships between skin labels and categories across different diagnostic tasks, constraining model performance as the number of target tasks grows. This paper proposes a label-guided graph learning network (LGL_Net) based on two-stage cross-modal fusion to achieve accurate diagnosis of skin diseases. Specifically, this paper first establishes a two-stage cross-modal fusion unit (TC_Fusion) to enable the fusion and transmission of morphological features from clinical data and deep lesion features from ultrasound data. Subsequently, a label-guided graph learning unit (LGL_Unit) is constructed to explore the correlations between multi-label data of skin lesions by building a graph convolutional network (GCN) at the label and category levels, thereby improving the accuracy of various skin disease diagnostic tasks. Extensive experiments were conducted on a public dataset (including clinical and dermoscopic data) and a private dataset (including clinical and ultrasound data). The experimental results demonstrate that the proposed method achieves optimal performance on tasks like pathological diagnosis (PG), benign or malignant diagnosis (BM), and 7-Point Checklist scoring (7 PC), offering a new approach for skin disease diagnosis. Our code is available at: https: //github. com/Zhaocheng1/LGL-Net.

JBHI Journal 2025 Journal Article

Self-Supervised Multi-Scale Multi-Modal Graph Pool Transformer for Sellar Region Tumor Diagnosis

Baiying Lei
Gege Cai
Yun Zhu
Tianfu Wang
Lei Dong
Cheng Zhao
Xinzhi Hu
Huijun Zhu

The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of patients. Magnetic resonance imaging (MRI) has proven to be an effective tool for the early detection of sellar region tumors. However, the existing sellar region tumor diagnosis still remains challenging due to the small amount of dataset and data imbalance. To overcome these challenges, we propose a novel self-supervised multi-scale multi-modal graph pool Transformer (MMGPT) network that can enhance the multi-modal fusion of small and imbalanced MRI data of sellar region tumors. MMGPT can strengthen feature interaction between multi-modal images, which makes our model more robust. A contrastive learning equipped auto-encoder (CAE) via self-supervised learning (SSL) is adopted to learn more detailed information between different samples. The proposed CAE transfers the pre-trained knowledge to the downstream tasks. Finally, a hybrid loss is equipped to relieve the performance degradation caused by data imbalance. The experimental results show that the proposed method outperforms state-of-the-art methods and obtains higher accuracy and AUC in the classification of sellar region tumors.

JBHI Journal 2024 Journal Article

End-to-End Prediction of EGFR Mutation Status With Denseformer

Shijie Zhao
Wenyuan Li
Zhuoyan Liu
Tianji Pang
Yang Yang
Ning Qiang
Jingyi Zhao
Bangguo Li

Accurate genotyping of the epidermal growth factor receptor (EGFR) is critical for the treatment planning of lung adenocarcinoma. Currently, clinical identification of EGFR genotyping highly relies on biopsy and sequence testing which is invasive and complicated. Recent advancements in the integration of computed tomography (CT) imagery with deep learning techniques have yielded a non-invasive and straightforward way for identifying EGFR profiles. However, there are still many limitations for further exploration: 1) most of these methods still require physicians to annotate tumor boundaries, which are time-consuming and prone to subjective errors; 2) most of the existing methods are simply borrowed from computer vision field which does not sufficiently exploit the multi-level features for final prediction. To solve these problems, we propose a Denseformer framework to identify EGFR mutation status in a real end-to-end fashion directly from 3D lung CT images. Specifically, we take the 3D whole-lung CT images as the input of the neural network model without manually labeling the lung nodules. This is inspired by the medical report that the mutational status of EGFR is associated not only with the local tumor nodules but also with the microenvironment surrounded by the whole lung. Besides, we design a novel Denseformer network to fully explore the distinctive information across the different level features. The Denseformer is a novel network architecture that combines the advantages of both convolutional neural network (CNN) and Transformer. Denseformer directly learns from the 3D whole-lung CT images, which preserves the spatial location information in the CT images. To further improve the model performance, we designed a combined Transformer module. This module employs the Transformer Encoder to globally integrate the information of different levels and layers and use them as the basis for the final prediction. The proposed model has been tested on a lung adenocarcinoma dataset collected at the Affiliated Hospital of Zunyi Medical University. Extensive experiments demonstrated the proposed method can effectively extract meaningful features from 3D CT images to make accurate predictions. Compared with other state-of-the-art methods, Denseformer achieves the best performance among current methods using deep learning to predict EGFR mutation status based on a single modality of CT images.

JBHI Journal 2024 Journal Article

Specificity-Aware Federated Learning With Dynamic Feature Fusion Network for Imbalanced Medical Image Classification

Guanghui Yue
Peishan Wei
Tianwei Zhou
Youyi Song
Cheng Zhao
Tianfu Wang
Baiying Lei

Recently, federated learning has become a powerful technique for medical image classification due to its ability to utilize datasets from multiple clinical clients while satisfying privacy constraints. However, there are still some obstacles in federated learning. Firstly, most existing methods directly average the model parameters collected by medical clients on the server, ignoring the specificities of the local models. Secondly, class imbalance is a common issue in medical datasets. In this article, to handle these two challenges, we propose a novel specificity-aware federated learning framework that benefits from an Adaptive Aggregation Mechanism (AdapAM) and a Dynamic Feature Fusion Strategy (DFFS). Considering the specificity of each local model, we set the AdapAM on the server. The AdapAM utilizes reinforcement learning to adaptively weight and aggregate the parameters of local models based on their data distribution and performance feedback for obtaining the global model parameters. For the class imbalance in local datasets, we propose the DFFS to dynamically fuse the features of majority classes based on the imbalance ratio in the min-batch and collaborate the rest of features. We conduct extensive experiments on a dermoscopic dataset and a fundus image dataset. Experimental results show that our method can achieve state-of-the-art results in these two real-world medical applications.

IS Journal 2022 Journal Article

Robust Maximum Mixture Correntropy Criterion Based One-Class Classification Algorithm

Tianlei Wang
Jiuwen Cao
Haozhen Dai
Baiying Lei
Huanqiang Zeng

One-class classification achieves anomaly/outlier detection by exploiting the characteristics of target data. As a local similarity measure defined in kernel space, correntropy is generally more robust than the mean square error (MSE) based criterion in dealing with large outliers. In this article, the maximum mixture correntropy criterion (MMCC) with multiple kernels are applied to the shallow and hierarchical one-class extreme learning machine to enhance the model robustness and learning speed. Experiments on benchmark University of California, Irvine (UCI) classification datasets, urban acoustic classification dataset, and four synthetic datasets are carried out to show the effectiveness and comparisons with several state-of-the-art methods are provided to demonstrate the superiority of the proposed algorithms.

JBHI Journal 2022 Journal Article

Unsupervised Domain Adaptation Based Image Synthesis and Feature Alignment for Joint Optic Disc and Cup Segmentation

Haijun Lei
Weixin Liu
Hai Xie
Benjian Zhao
Guanghui Yue
Baiying Lei

Due to the discrepancy of different devices for fundus image collection, a well-trained neural network is usually unsuitable for another new dataset. To solve this problem, the unsupervised domain adaptation strategy attracts a lot of attentions. In this paper, we propose an unsupervised domain adaptation method based image synthesis and feature alignment (ISFA) method to segment optic disc and cup on fundus images. The GAN-based image synthesis (IS) mechanism along with the boundary information of optic disc and cup is utilized to generate target-like query images, which serves as the intermediate latent space between source domain and target domain images to alleviate the domain shift problem. Specifically, we use content and style feature alignment (CSFA) to ensure the feature consistency among source domain images, target-like query images and target domain images. The adversarial learning is used to extract domain-invariant features for output-level feature alignment (OLFA). To enhance the representation ability of domain-invariant boundary structure information, we introduce the edge attention module (EAM) for low-level feature maps. Eventually, we train our proposed method on the training set of the REFUGE challenge dataset and test it on Drishti-GS and RIM-ONE_r3 datasets. On the Drishti-GS dataset, our method achieves about 3% improvement of Dice on optic cup segmentation over the next best method. We comprehensively discuss the robustness of our method for small dataset domain adaptation. The experimental results also demonstrate the effectiveness of our method. Our code is available at https://github.com/thinkobj/ISFA.

JBHI Journal 2021 Journal Article

Attention-Guided Multi-Branch Convolutional Neural Network for Mitosis Detection From Histopathological Images

Haijun Lei
Shaomin Liu
Ahmed Elazab
Xuehao Gong
Baiying Lei

Mitotic count is an important indicator for assessing the invasiveness of breast cancers. Currently, the number of mitoses is manually counted by pathologists, which is both tedious and time-consuming. To address this situation, we propose a fast and accurate method to automatically detect mitosis from the histopathological images. The proposed method can automatically identify mitotic candidates from histological sections for mitosis screening. Specifically, our method exploits deep convolutional neural networks to extract high-level features of mitosis to detect mitotic candidates. Then, we use spatial attention modules to re-encode mitotic features, which allows the model to learn more efficient features. Finally, we use multi-branch classification subnets to screen the mitosis. Compared to existing related methods in literature, our method obtains the best detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition. Code has been made available at: https://github.com/liushaomin/MitosisDetection.

JBHI Journal 2020 Journal Article

A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes

Jinbao Dong
Shengfeng Liu
Yimei Liao
Huaxuan Wen
Baiying Lei
Shengli Li
Tianfu Wang

Quality control/assessment of ultrasound (US) images is an essential step in clinical diagnosis. This process is usually done manually, suffering from some drawbacks, such as dependence on operator's experience and extensive labors, as well as high inter- and intra-observer variation. Automatic quality assessment of US images is therefore highly desirable. Fetal US cardiac four-chamber plane (CFP) is one of the most commonly used cardiac views, which was used in the diagnosis of heart anomalies in the early 1980s. In this paper, we propose a generic deep learning framework for automatic quality control of fetal US CFPs. The proposed framework consists of three networks: (1) a basic CNN (B-CNN), roughly classifying four-chamber views from the raw data; (2) a deeper CNN (D-CNN), determining the gain and zoom of the target images in a multi-task learning manner; and (3) the aggregated residual visual block net (ARVBNet), detecting the key anatomical structures on a plane. Based on the output of the three networks, overall quantitative score of each CFP is obtained, so as to achieve fully automatic quality control. Experiments on a fetal US dataset demonstrated our proposed method achieved a highest mean average precision (mAP) of 93. 52% at a fast speed of 101 frames per second (FPS). In order to demonstrate the adaptability and generalization capacity, the proposed detection network (i. e. , ARVBNet) has also been validated on the PASCAL VOC dataset, obtaining a highest mAP of 81. 2% when input size is approximately 300 × 300.

AAAI Conference 2020 Conference Paper

High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation

Mohammad Hamghalam
Baiying Lei
Tianfu Wang

Magnetic resonance imaging (MRI) provides varying tissue contrast images of internal organs based on a strong magnetic ﬁeld. Despite the non-invasive advantage of MRI in frequent imaging, the low contrast MR images in the target area make tissue segmentation a challenging problem. This paper demonstrates the potential beneﬁts of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images. Notably, we adopt a new cycle generative adversarial network (CycleGAN) with an attention mechanism to increase the contrast within underlying tissues. The attention block, as well as training on HTC images, guides our model to converge on certain tissues. To increase the resolution of HTC images, we employ multi-stage architecture to focus on one particular tissue as a foreground and ﬁlter out the irrelevant background in each stage. This multi-stage structure also alleviates the common artifacts of the synthetic images by decreasing the gap between source and target domains. We show the application of our method for synthesizing HTC images on brain MR scans, including glioma tumor. We also employ HTC MR images in both the end-to-end and two-stage segmentation structure to conﬁrm the effectiveness of these images. The experiments over three competitive segmentation baselines on BraTS 2018 dataset indicate that incorporating the synthetic HTC images in the multimodal segmentation framework improves the average Dice scores 0. 8%, 0. 6%, and 0. 5% on the whole tumor, tumor core, and enhancing tumor, respectively, while eliminating one real MRI sequence from the segmentation procedure.

JBHI Journal 2019 Journal Article

Dense Deconvolutional Network for Skin Lesion Segmentation

Hang Li
Xinzi He
Feng Zhou
Zhen Yu
Dong Ni
Siping Chen
Tianfu Wang
Baiying Lei

Automatic delineation of skin lesion contours from dermoscopy images is a basic step in the process of diagnosis and treatment of skin lesions. However, it is a challenging task due to the high variation of appearances and sizes of skin lesions. In order to deal with such challenges, we propose a new dense deconvolutional network (DDN) for skin lesion segmentation based on residual learning. Specifically, the proposed network consists of dense deconvolutional layers (DDLs), chained residual pooling (CRP), and hierarchical supervision (HS). First, unlike traditional deconvolutional layers, DDLs are adopted to maintain the dimensions of the input and output images unchanged. The DDNs are trained in an end-to-end manner without the need of prior knowledge or complicated postprocessing procedures. Second, the CRP aims to capture rich contextual background information and to fuse multilevel features. By combining the local and global contextual information via multilevel feature fusion, the high-resolution prediction output is obtained. Third, HS is added to serve as an auxiliary loss and to refine the prediction mask. Extensive experiments based on the public ISBI 2016 and 2017 skin lesion challenge datasets demonstrate the superior segmentation results of our proposed method over the state-of-the-art methods.

JBHI Journal 2019 Journal Article

Neuroimaging Retrieval via Adaptive Ensemble Manifold Learning for Brain Disease Diagnosis

Baiying Lei
Peng Yang
Yinan Zhuo
Feng Zhou
Dong Ni
Siping Chen
Xiaohua Xiao
Tianfu Wang

Alzheimer's disease (AD) is a neurodegenerative and non-curable disease, with serious cognitive impairment, such as dementia. Clinically, it is critical to study the disease with multi-source data in order to capture a global picture of it. In this respect, an adaptive ensemble manifold learning (AEML) algorithm is proposed to retrieve multi-source neuroimaging data. Specifically, an objective function based on manifold learning is formulated to impose geometrical constraints by similarity learning. The complementary characteristics of various sources of brain disease data for disorder discovery are investigated by tuning weights from ensemble learning. In addition, a generalized norm is explicitly explored for adaptive sparseness degree control. The proposed AEML algorithm is evaluated by the public AD neuroimaging initiative database. Results obtained from the extensive experiments demonstrate that our algorithm outperforms the traditional methods.

JBHI Journal 2019 Journal Article

Parkinson's Disease Diagnosis via Joint Learning From Multiple Modalities and Relations

Haijun Lei
Zhongwei Huang
Feng Zhou
Ahmed Elazab
Ee-Leng Tan
Hancong Li
Jing Qin
Baiying Lei

Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multitask feature selection model to explore multiple relationships among features, samples, and clinical scores. We regress four clinical variables of depression, sleep, olfaction, cognition scores, as well as perform the classification of PD disease from the multimodal data. The multitask model explores the relationships at the level of clinical scores, image features, and subjects, to select the most informative and diseased-related features for diagnosis. The proposed method is evaluated on the public Parkinson's progression markers initiative dataset. The extensive experimental results show that the multitask framework can effectively boost the performance of regression and classification and outperforms other state-of-the-art methods. The computerized predictions of clinical scores and label for PD diagnosis may offer quantitative reference for decision support as well.

JBHI Journal 2019 Journal Article

Protein–Protein Interactions Prediction via Multimodal Deep Polynomial Network and Regularized Extreme Learning Machine

Haijun Lei
Yuting Wen
Zhuhong You
Ahmed Elazab
Ee-Leng Tan
Yujia Zhao
Baiying Lei

Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs prediction is highly desirable. PPIs endow with protein amino acid mutation rate and two physicochemical properties of protein (e. g. , hydrophobicity and hydrophilicity). Deep polynomial network (DPN) is well-suited to integrate these modalities since it can represent any function on a finite sample dataset via the supervised deep learning algorithm. We propose a multimodal DPN (MDPN) algorithm to effectively integrate these modalities to enhance prediction performance. MDPN consists of a two-stage DPN, the first stage feeds multiple protein features into DPN encoding to obtain high-level feature representation while the second stage fuses and learns features by cascading three types of high-level features in the DPN encoding. We employ a regularized extreme learning machine to predict PPIs. The proposed method is tested on the public dataset of H. pylori, Human, and Yeast and achieves average accuracies of 97. 87%, 99. 90%, and 98. 11%, respectively. The proposed method also achieves good accuracies on other datasets. Furthermore, we test our method on three kinds of PPI networks and obtain superior prediction results.

JBHI Journal 2018 Journal Article

A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition

Zhen Yu
Ee-Leng Tan
Dong Ni
Jing Qin
Siping Chen
Shengli Li
Baiying Lei
Tianfu Wang

Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intraclass variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs. To improve the recognition performance, we propose a method to automatically recognize FFSP via a deep convolutional neural network (DCNN) architecture. The proposed DCNN consists of 16 convolutional layers with small 3 × 3 size kernels and three fully connected layers. A global average pooling is adopted in the last pooling layer to significantly reduce network parameters, which alleviates the overfitting problems and improves the performance under limited training data. Both the transfer learning strategy and a data augmentation technique tailored for FFSP are implemented to further boost the recognition performance. Extensive experiments demonstrate the advantage of our proposed method over traditional approaches and the effectiveness of DCNN to recognize FFSP for clinical diagnosis.

AAAI Conference 2018 Conference Paper

Automated Segmentation of Overlapping Cytoplasm in Cervical Smear Images via Contour Fragments

Youyi Song
Jing Qin
Baiying Lei
Kup-Sze Choi

We present a novel method for automated segmentation of overlapping cytoplasm in cervical smear images based on contour fragments. We formulate the segmentation problem as a graphical model, and employ the contour fragments generated from cytoplasm clump to construct the graph. Compared with traditional methods that are based on pixels, our contour fragment-based solution can take more geometric information into account and hence generate more accurate prediction of the overlapping boundaries. We further design a novel energy function for the graph, and by minimizing the energy function, fragments that come from the same cytoplasm are selected into the same set. To construct the energy function, our fragments-based data term and pairwise term are measured from the spatial relation and shape prior, which offer more geometric information for the occluded boundary inference. Afterwards, occluded boundaries are inferred using the minimal path model, in which shape of each individual cytoplasm is reconstructed on the selected fragments set. Constructed shape is used as a constraint to locate the searching area, and curvature regulation is enforced to promote the smoothness of inference result. The inference result, in turn, is used as the shape prior to construct a high-level shape regulation energy term of the built graph, and then graph energy is updated. In other words, fragments selection and occluded boundary inference are iterative processed; this interaction makes more potential shape information accessible. Using two cervical smear datasets, the performance of our method is extensively evaluated and compared with that of the stateof-the-art approaches; the results show the superiority of the proposed method.

JBHI Journal 2018 Journal Article

Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting

Jing Li
Yi Wang
Baiying Lei
Jie-Zhi Cheng
Jing Qin
Tianfu Wang
Shengli Li
Dong Ni

Head circumference (HC) is one of the most important biometrics in assessing fetal growth during prenatal ultrasound examinations. However, the manual measurement of this biometric by doctors often requires substantial experience. We developed a learning-based framework that used prior knowledge and employed a fast ellipse fitting method (ElliFit) to measure HC automatically. We first integrated the prior knowledge about the gestational age and ultrasound scanning depth into a random forest classifier to localize the fetal head. We further used phase symmetry to detect the center line of the fetal skull and employed ElliFit to fit the HC ellipse for measurement. The experimental results from 145 HC images showed that our method had an average measurement error of 1. 7 mm and outperformed traditional methods. The experimental results demonstrated that our method shows great promise for applications in clinical practice.

JBHI Journal 2017 Journal Article

Segmentation, Splitting, and Classification of Overlapping Bacteria in Microscope Images for Automatic Bacterial Vaginosis Diagnosis

Youyi Song
Liang He
Feng Zhou
Siping Chen
Dong Ni
Baiying Lei
Tianfu Wang

Quantitative analysis of bacterial morphotypes in the microscope images plays a vital role in diagnosis of bacterial vaginosis (BV) based on the Nugent score criterion. However, there are two main challenges for this task: 1) It is quite difficult to identify the bacterial regions due to various appearance, faint boundaries, heterogeneous shapes, low contrast with the background, and small bacteria sizes with regards to the image. 2) There are numerous bacteria overlapping each other, which hinder us to conduct accurate analysis on individual bacterium. To overcome these challenges, we propose an automatic method in this paper to diagnose BV by quantitative analysis of bacterial morphotypes, which consists of a three-step approach, i. e. , bacteria regions segmentation, overlapping bacteria splitting, and bacterial morphotypes classification. Specifically, we first segment the bacteria regions via saliency cut, which simultaneously evaluates the global contrast and spatial weighted coherence. And then Markov random field model is applied for high-quality unsupervised segmentation of small object. We then decompose overlapping bacteria clumps into markers, and associate a pixel with markers to identify evidence for eventual individual bacterium splitting. Next, we extract morphotype features from each bacterium to learn the descriptors and to characterize the types of bacteria using an Adaptive Boosting machine learning framework. Finally, BV diagnosis is implemented based on the Nugent score criterion. Experiments demonstrate that our proposed method achieves high accuracy and efficiency in computation for BV diagnosis.