Research on the classification of lymphoma pathological images based on deep residual neural network

Zhang, Xiaoli; Zhang, Kuixing; Jiang, Mei; Yang, Lin

doi:10.3233/thc-218031

Cited by 17 publications

(17 citation statements)

References 21 publications

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“… 14 Therefore, pathologists who need to survey pathological images to determine the subtype of lymphoma require a commitment of time and energy. 15 It is difficult to draw a correct conclusion quickly only by traditional naked-eye observation and subjective judgment. This challenges the experience and ability of technicians to quickly and accurately detect abnormal cells.…”

Section: Applications Of ML In Lymphoma Diagnosismentioning

confidence: 99%

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Application of machine learning in the management of lymphoma: Current practice and future prospects

Yuan,

Zhang,

Wang

2024

DIGITAL HEALTH

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In the past decade, digitization of medical records and multiomics data analysis in lymphoma has led to the accessibility of high-dimensional records. The digitization of medical records, the visualization of extensive volume data extracted from medical images, and the integration of multiomics methods into clinical decision-making have produced many datasets. As a promising auxiliary tool, machine learning (ML) intends to extract homologous features in large-scale data sets and encode them into various patterns to complete complicated tasks. At present, artificial intelligence and digital mining have shown promising prospects in the field of lymphoma pathological image analysis. The paradigm shift from qualitative analysis to quantitative analysis makes the pathological diagnosis more intelligent and the results more accurate and objective. ML can promote accurate lymphoma diagnosis and provide patients with prognostic information and more individualized treatment options. Based on the above, this comprehensive review of the general workflow of ML highlights recent advances in ML techniques in the diagnosis, treatment, and prognosis of lymphoma, and clarifies the boundedness and future orientation of the ML technique in the clinical practice of lymphoma.

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Section: Applications Of ML In Lymphoma Diagnosismentioning

confidence: 99%

“…The network model could offer objective evidence for clinicians to diagnose the type of NHL. 15 In the research of Alferez et al, they trained a recognition system with an SVM algorithm. The system is an automatic analysis method based on an image.…”

Section: Applications Of ML In Lymphoma Diagnosismentioning

confidence: 99%

Application of machine learning in the management of lymphoma: Current practice and future prospects

Yuan,

Zhang,

Wang

2024

DIGITAL HEALTH

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“…The manuscripts were organized according to the type of input data, i.e., PET/CT scan, histological images, immunophenotype, clinicopathological variables, and gene expression, mutational, and integrative analysis-based artificial intelligence [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 ].…”

Section: Review Of the Literature And Future Perspective In Hematolog...mentioning

confidence: 99%

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

Carreras

Roncador

Hamoudi

2022

Cancers

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Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.

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“…Zhang et al conducted a comparison of the performance of a BP network and optimized neural network with the GA-BP genetic method. The dataset was optimized before being fed into the systems, and the GA-BP network performed better than the BP network when classifying histopathological images of malignant lymphoma [11]. Hashimoto et al applied a deep learning model according to multi-instance learning to diagnose malignant lymphoma subtypes.…”

Section: Related Workmentioning

confidence: 99%

“…Accuracy (%) Sensitivity (%) Precision % Specificity (%) AUC % Miyoshi et al [10] 87 91 89 -Zhang et al [11] 96 ----Hashimoto et al [12] 68.…”

Section: Previous Studiesmentioning

confidence: 99%

Diagnosis of Histopathological Images to Distinguish Types of Malignant Lymphomas Using Hybrid Techniques Based on Fusion Features

et al. 2022

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Malignant lymphoma is one of the types of malignant tumors that can lead to death. The diagnostic method for identifying malignant lymphoma is a histopathological analysis of lymphoma tissue images. Because of the similar morphological characteristics of the lymphoma types, it is difficult for doctors and specialists to manually distinguish the types of lymphomas. Therefore, deep and automated learning techniques aim to solve this problem and help clinicians reconsider their diagnostic decisions. Because of the similarity of the morphological characteristics between lymphoma types, this study aimed to extract features using various algorithms and deep learning models and combine them together into feature vectors. Two datasets have been applied, each with two different systems for the reliable diagnosis of malignant lymphoma. The first system was a hybrid system between DenseNet-121 and ResNet-50 to extract deep features and reduce their dimensions by the principal component analysis (PCA) method, using the support vector machine (SVM) algorithm for classifying low-dimensional deep features. The second system was based on extracting the features using DenseNet-121 and ResNet-50 and combining them with the hand-crafted features extracted by gray level co-occurrence matrix (GLCM), fuzzy color histogram (FCH), discrete wavelet transform (DWT), and local binary pattern (LBP) algorithms and classifying them using a feed-forward neural network (FFNN) classifier. All systems achieved superior results in diagnosing the two datasets of malignant lymphomas. An FFNN classifier with features of ResNet-50 and hand-crafted features reached an accuracy of 99.5%, specificity of 100%, sensitivity of 99.33%, and AUC of 99.86% for the first dataset. In contrast, the same technique reached 100% for all measures to diagnose the second dataset.

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Research on the classification of lymphoma pathological images based on deep residual neural network

Cited by 17 publications

References 21 publications

Application of machine learning in the management of lymphoma: Current practice and future prospects

Application of machine learning in the management of lymphoma: Current practice and future prospects

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

Diagnosis of Histopathological Images to Distinguish Types of Malignant Lymphomas Using Hybrid Techniques Based on Fusion Features

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