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Dan Shao

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5 papers

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JBHI Journal 2026 Journal Article

An Automatic 3D PET Tumor Segmentation Framework Assisted by Geodesic Sequences

Lin Yang
Dan Shao
Chuanli Cheng
Chao Zou
Zhenxing Huang
Hairong Zheng
Dong Liang
Zhi-Feng Pang

Positron Emission Tomography (PET) images reflect the metabolic rate of tracers in different tissues of the human body, crucial for early cancer diagnosis and treatment. Accurate tumor segmentation is essential to aid clinicians in determining drug dosages. Due to the low resolution of PET images, prior information (such as CT, MRI or distance information) are often incorporated to assist PET segmentation. In this paper, we propose an automatic 3D PET tumor segmentation framework assisted by geodesic sequences. Specifically, considering the intrinsic characteristics of PET images, we first construct geodesic prior, which effectively enhances the contrast between the tumor and background while suppressing noise and the influence of other tissues. To address the need for seed points in the geodesic prior, an automatic marking strategy is designed that identifies all suspected lesion regions and uses their central points as a series of seeds to generate the corresponding geodesic sequences. Subsequently, we develop a three-branch network architecture to simultaneously process PET images, geodesic sequences, and background geodesic information. To enhance image features, a distance attention mechanism is introduced at the end of the network encoder to effectively measure the similarity between different geodesic features, refining the image features. Finally, the network incorporates spatial regularization and local PET intensity information into the activation function via the Soft Threshold Dynamics with Local Intensity Fitting (STDLIF) module, further improving segmentation accuracy. Experimental results demonstrate that, compared to existing state-of-the-art algorithms, the proposed method shows better segmentation performance on both clinical and public datasets.

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JBHI Journal 2025 Journal Article

A Deep Learning-Based Approach for the Diagnostic of Brucellar Spondylitis in Magnetic Resonance Images

Dan Shao
Jinquan Wei
Binyang Wang
Zhijun Wang
Pengying Niu
Lvlin Yang
Guangzhao Zhang
Pu Chen

Brucellar spondylitis (BS), a prevalent zoonotic disease caused by Brucella, poses a significant global health threat. Accurate and timely diagnosis of BS is crucial for effective treatment; however, no specialized deep learning model has been developed for detecting BS in MR images. In this study, we proposed Brucella Spondylitis MRI Diagnosis Network (BSMRINet), a fully automated diagnostic framework designed for the detection of BS from T2-weighted (T2W) MR images. The model was developed and validated using 582 cohorts collected from four hospitals between January 2018 and August 2023. The BSMRINet architecture comprised two key modules. The vertebral body lesion detection module was designed to detect BS in intact vertebral bodies by integrating a corner detection algorithm with a ResNet-based deep learning model. This module provided accurate identification and localization of potential lesions of Brucella and calculated intervertebral disc height (DH) values. The spine lesion detection module was specifically designed to detect BS in damaged vertebral bodies by utilizing a DenseNet architecture with modified squeeze-and-excitation (scSE) networks. This module further evaluated paravertebral injuries, including abscess formation, soft tissue swelling, and joint involvement. BSMRINet demonstrated strong robustness and generalization across both internal and external validation phases. Additionally, it outperformed two radiologists with 10 to 15 years of experience in diagnosing spinal MR images. The results suggested that BSMRINet can assist in the diagnostic process of BS and enhance the diagnostic capabilities of radiologists.

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JBHI Journal 2025 Journal Article

Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS

Qing Zhang
Dan Shao
Lin Lin
Guoliang Gong
Rui Xu
Shoji Kido
HongWei Cui

In the field of diagnosing lung diseases, the application of neural networks (NNs) in image classification exhibits significant potential. However, NNs are considered “black boxes, ” making it difficult to discern their decision-making processes, thereby leading to skepticism and concern regarding NNs. This compromises model reliability and hampers intelligent medicine's development. To tackle this issue, we introduce the Evolutionary Neural Architecture Search (EvoNAS). In image classification tasks, EvoNAS initially utilizes an Evolutionary Algorithm to explore various Convolutional Neural Networks, ultimately yielding an optimized network that excels at separating between redundant texture features and the most discriminative ones. Retaining the most discriminative features improves classification accuracy, particularly in distinguishing similar features. This approach illuminates the intrinsic mechanics of classification, thereby enhancing the accuracy of the results. Subsequently, we incorporate a Differential Evolution algorithm based on distribution estimation, significantly enhancing search efficiency. Employing visualization techniques, we demonstrate the effectiveness of EvoNAS, endowing the model with interpretability. Finally, we conduct experiments on the diffuse lung disease texture dataset using EvoNAS. Compared to the original network, the classification accuracy increases by 0. 56%. Moreover, our EvoNAS approach demonstrates significant advantages over existing methods in the same dataset.

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JBHI Journal 2025 Journal Article

SecProGNN: Predicting Bronchoalveolar Lavage Fluid Secreted Protein Using Graph Neural Network

Dan Shao
Guangzhao Zhang
Lin Lin
Yucong Xiong
Kai He
Liyan Sun

Bronchoalveolar lavage fluid (BALF) is a liquid obtained from the alveoli and bronchi, often used to study pulmonary diseases. So far, proteomic analyses have identified over three thousand proteins in BALF. However, the comprehensive characterization of these proteins remains challenging due to their complexity and technological limitations. This paper presented a novel deep learning framework called SecProGNN, designed to predict secretory proteins in BALF. Firstly, SecProGNN represented proteins as graph-structured data, with amino acids connected based on their interactions. Then, these graphs were processed through graph neural networks (GNNs) model to extract graph features. Finally, the extracted feature vectors were fed into a multi-layer perceptron (MLP) module to predict BALF secreted proteins. Additionally, by utilizing SecProGNN, we investigated potential biomarkers for lung adenocarcinoma and identified 16 promising candidates that may be secreted into BALF.

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JBHI Journal 2025 Journal Article

TSLAmy: A Novel Amyloid Hexapeptide Aggregation Prediction Approach Based on Two-Stage Learning

Lan Huang
Qingchen Jiang
Yucong Xiong
Guangzhao Zhang
Dan Shao

Identifying aggregation-prone proteins or peptides is essential for advancing our understanding of amyloid aggregation processes and their related pathogenic mechanisms. Recognizing potential amyloid hexapeptides can also support peptide-based drug design and reduce experimental costs. In this study, we proposed TSLAmy, a computational model designed to predict amyloid hexapeptides using a two-stage learning framework. In the first stage, we performed feature extraction on the hexapeptides, and in the second stage, we presented prediction model for amyloid hexapeptide aggregation. Firstly, to ensure balanced dataset partitioning, we applied a clustering-based method by training two autoencoders on all possible hexapeptides using their sequence and physicochemical features, respectively. The resulting clusters were used to stratify the data into training and testing datasets. Then, in the first stage, we extracted features from hexapeptides based on their sequence and physicochemical properties. The feature extraction module was used to obtain physicochemical features, while the ESM-2 module was responsible for extracting sequence features for each hexapeptide. Finally, in the second stage, the aggregation prediction module was employed to predict the aggregation potential of hexapeptides. The experimental results demonstrated that the accuracy of TSLAmy reached 0. 8493 (0. 8447-0. 8539), outperforming other state-of-the-art methods. Furthermore, we predicted the aggregation potential of all 64, 000, 000 possible hexapeptides and analyzed the amino acids that form aggregation-prone hexapeptides. We anticipate that TSLAmy can offer new insights into the identification of aggregation-prone peptides, contributing to advancements in peptide drug development.

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