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Application of graph-curvature features in computer-aided diagnosis for histopathological image identification of gastric cancer

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Intelligent Medicine 2024年第3期4卷 141-152页

作者： Ruilin He Chen Li Xinyi Yang Jinzhu Yang Tao Jiang marcin grzegorzek Hongzan Sun Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110016China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110016China School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China Institute for Medical Informatics University of LuebeckLuebeckGermany Department of Knowledge Engineering University of Economics in KatowiceKatowicePoland Shengjing Hospital of China Medical University ShenyangLiaoning 110136China

Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some *** study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological *** The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for ***,the U-net was applied to segment the ***,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was *** third step was to extract the graph-curvature features of the histopathological image according to the MST ***,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be *** During the experiment,we used various methods for *** the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were *** found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological *** the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern *** the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and *** comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.

关键词： Gastric cancer Graph-curvature feature Image identification

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Increasing the accuracy and reproducibility of positron emission tomography radiomics for predicting pelvic lymph node metastasis in patients with cervical cancer using 3D local binary pattern-based texture features

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Intelligent Medicine 2024年第3期4卷 153-160页

作者： Yang Yu Xiaoran Li Tianming Du Md Rahaman marcin Jerzy grzegorzek Chen Li Hongzan Sun Department of Radiology Shengjing Hospital of China Medical UniversityShenyangLiaoning 110001China Liaoning Provincial Key Laboratory of Medical Imaging School of Computer Science and Engineering Northeastern UniversityShenyangLiaoning 110169China Department of Radiology Xuanwu HospitalCapital medical UniversityBeijing 100053China Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Peking University Health Science CenterBeijing 100191China College of Medicine and Biological Information Engineering Northeastern UniversityShenyangLiaoning 110169China The University of New South Wales Australia University of Lübeck Germany

Background The reproducibility of positron emission tomography(PET)radiomics features is affected by several factors,such as scanning equipment,drug metabolism time and reconstruction *** aimed to explore the role of 3D local binary pattern(LBP)-based texture in increasing the accuracy and reproducibility of PET radiomics for predicting pelvic lymph node metastasis(PLNM)in patients with cervical *** We retrospectively analysed data from 177 patients with cervical squamous cell *** un-derwent18 F-fluorodeoxyglucose(18 F-FDG)whole-body PET/computed tomography(PET/CT),followed by pelvic 18 F-FDG PET/magnetic resonance imaging(PET/MR).We selected reproducible and informative PET radiomics features using Lin’s concordance correlation coefficient,least absolute shrinkage and selection operator algorithm,and established 4 models,PET/CT,PET/CT-fusion,PET/MR and PET/MR-fusion,using the logistic regression *** performed receiver operating characteristic(ROC)curve analysis to evaluate the models in the training data set(65 patients who underwent radical hysterectomy and pelvic lymph node dissection)and test data set(112 patients who received concurrent chemoradiotherapy or no treatment).The DeLong test was used for pairwise comparison of the ROC curves among the *** The distribution of age,squamous cell carcinoma(SCC),International Federation of Gynaecology and Obstetrics stage and PLNM between the training and test data sets were different(PPET/MR=PET/MR-fusion(0.954).There was no statistical difference between the ROC curve of PET/CT and PET/MR models in both data sets(P>0.05).Conclusions The LBP-transformed radiomics features based on PET ima

关键词： Positron emission tomography Radiomics Cervical cancer Reproducibility Pelvic lymph node metastasis

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Application of transfer learning and ensemble learning in image-level classification for breast histopathology

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Intelligent Medicine 2023年第2期3卷 115-128页

作者： Yuchao Zheng Chen Li Xiaomin Zhou Haoyuan Chen Hao Xu Yixin Li Haiqing Zhang Xiaoyan Li Hongzan Sun Xinyu Huang marcin grzegorzek Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110819 China China Medical University ShenyangLiaoning 110122 China Institute of Medical Informatics University of LuebeckLuebeckGermany

Background Breast cancer has the highest prevalence among all cancers in women *** classification of histopathological images in the diagnosis of breast cancers is an area of clinical *** computer-aided diagnosis,most traditional classification models use a single network to extract features,although this approach has significant ***,many networks are trained and optimized on patient-level datasets,ignoring lower-level data *** This paper proposed a deep ensemble model based on image-level labels for the binary classification of breast histopathological images of benign and malignant ***,the BreaKHis dataset was randomly divided into training,validation,and test ***,data augmentation techniques were used to balance the numbers of benign and malignant ***,based on their transfer learning performance and the complementarity between networks,VGG16,Xception,ResNet50,and DenseNet201 were selected as base *** In a ensemble network model with accuracy as the weight,the image-level binary classification achieved an accuracy of 98.90%.To verify the capabilities of our method,it was experimentally compared with the latest transformer and multilayer perception(MLP)models on the same *** ensemble model showed a 5%-20%advantage,emphasizing its far-reaching abilities in classification *** This research focuses on improving the performance of a classification model with an ensemble *** learning has an essential role in classification of small datasets,improving training speed and *** model may outperform many existing approaches with respect to accuracy and has applications in the field of auxiliary medical diagnosis.

关键词： Convolutional neural network Transfer learning Ensemble learning Image classification Histopathological image Breast cancer

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Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer:a comprehensive comparative study

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Intelligent Medicine 2024年第2期4卷 114-127页

作者： Ao Chen Chen Li Md Mamunur Rahaman Yudong Yao Haoyuan Chen Hechen Yang Peng Zhao Weiming Hu Wanli Liu Shuojia Zou Ning Xu marcin grzegorzek Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110169China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Arts and Design Liaoning Petrochemical UniversityFushunLiaoning 113001China Institute of Medical Informatics University of LübeckLüebeckGermany

Background With the gradual increase of infertility in the world,among which male sperm problems are the main factor for infertility,more and more couples are using computer-assisted sperm analysis(CASA)to assist in the analysis and treatment of ***,the rapid development of deep learning(DL)has led to strong results in image classification ***,the classification of sperm images has not been well studied in current deep learning methods,and the sperm images are often affected by noise in practical CASA *** purpose of this article is to investigate the anti-noise robustness of deep learning classification methods applied on sperm *** The SVIA dataset is a publicly available large-scale sperm dataset containing three *** this work,we used subset-C,which provides more than 125,000 independent images of sperms and impurities,including 121,401 sperm images and 4,479 impurity *** investigate the anti-noise robustness of deep learning classification methods applied on sperm images,we conducted a comprehensive comparative study of sperm images using many convolutional neural network(CNN)and visual transformer(VT)deep learning methods to find the deep learning model with the most stable anti-noise *** This study proved that VT had strong robustness for the classification of tiny object(sperm and impurity)image datasets under some types of conventional noise and some adversarial *** particular,under the influence of Poisson noise,accuracy changed from 91.45%to 91.08%,impurity precison changed from 92.7%to 91.3%,impurity recall changed from 88.8%to 89.5%,and impurity F1-score changed 90.7%to 90.4%.Meanwhile,sperm precision changed from 90.9%to 90.5%,sperm recall changed from 92.5%to 93.8%,and sperm F1-score changed from 92.1%to 90.4%.Conclusion Sperm image classification may be strongly affected by noise in current deep learning methods;the robustness with regard to noise of VT methods based

关键词： Computer-assisted sperm analysis Anti-noise Robustness Deep learning .Image classification Sperm image Conventional noise Adversarial attacks Convolutional neural network Visual transformer

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