Matches in SemOpenAlex for { <https://semopenalex.org/work/W4327520743> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W4327520743 abstract "Lymphoma is a common malignancy that endangers human life and health, and accurate identification of lymphoma from PET/CT images has great value in clinical treatment. Efficient and accurate discrimination of lymphomas is an important and challenging research task. However, deep networks may lose important features such as texture structure of the target while acquiring rich image information and even misclassify small-scale targets. Traditional machine learning methods rely heavily on manually designed features and require the design of reasonable and effective feature combinations. To this end, we propose a new bidirectional feature fusion method with a lymphoma subtype classification model for PET/CT images. Firstly, deep learning latent features and machine learning explicit features of PET/CT images are extracted based on convolutional neural networks and prior knowledge, where the explicit features include distribution features and radiomics features. In the latent features extraction stage, we propose a new feature channel compression method based on squeeze-and-excitation normalization. Then, the latent features and explicit features are effectively fused based on the proposed bidirectional feature selection method. Finally, a classifier is constructed by introducing deep learning and machine learning methods for lymphoma classification. To validate the effectiveness of the model, we designed multiple sets of comparison experiments and ablation experiments to classify lymphoma subtypes, including non-lymphoma, Hodgkin's lymphoma, diffuse large B-cell lymphoma and other non-Hodgkin's lymphoma on the lymphoma dataset. The accuracy and recall of the classification reached 0.831 and 0.819, respectively. To validate the generalization of the model, we set experiments on the lung cancer PET/CT dataset, and our model improved the accuracy of classification by 0.045 compared with the resnet18 network. The experiments show that the proposed method in this paper has better classification on lymphoma dataset and can be applied to other tumors." @default.
- W4327520743 created "2023-03-17" @default.
- W4327520743 creator A5033990108 @default.
- W4327520743 creator A5043274136 @default.
- W4327520743 creator A5059127060 @default.
- W4327520743 creator A5064946449 @default.
- W4327520743 creator A5073727134 @default.
- W4327520743 creator A5082232185 @default.
- W4327520743 creator A5085183210 @default.
- W4327520743 creator A5086420717 @default.
- W4327520743 date "2022-11-10" @default.
- W4327520743 modified "2023-09-27" @default.
- W4327520743 title "Classification of lymphoma subtypes in PET/CT images based on a bidirectional feature fusion method" @default.
- W4327520743 cites W2136038762 @default.
- W4327520743 cites W2883645764 @default.
- W4327520743 cites W2982589596 @default.
- W4327520743 cites W3002312636 @default.
- W4327520743 cites W3021925134 @default.
- W4327520743 cites W3086743023 @default.
- W4327520743 cites W3092827891 @default.
- W4327520743 cites W3101197104 @default.
- W4327520743 cites W3115793789 @default.
- W4327520743 cites W3119214506 @default.
- W4327520743 cites W3119494456 @default.
- W4327520743 cites W3121092655 @default.
- W4327520743 cites W3160643432 @default.
- W4327520743 cites W4224210955 @default.
- W4327520743 doi "https://doi.org/10.1145/3574198.3574206" @default.
- W4327520743 hasPublicationYear "2022" @default.
- W4327520743 type Work @default.
- W4327520743 citedByCount "0" @default.
- W4327520743 crossrefType "proceedings-article" @default.
- W4327520743 hasAuthorship W4327520743A5033990108 @default.
- W4327520743 hasAuthorship W4327520743A5043274136 @default.
- W4327520743 hasAuthorship W4327520743A5059127060 @default.
- W4327520743 hasAuthorship W4327520743A5064946449 @default.
- W4327520743 hasAuthorship W4327520743A5073727134 @default.
- W4327520743 hasAuthorship W4327520743A5082232185 @default.
- W4327520743 hasAuthorship W4327520743A5085183210 @default.
- W4327520743 hasAuthorship W4327520743A5086420717 @default.
- W4327520743 hasConcept C108583219 @default.
- W4327520743 hasConcept C119857082 @default.
- W4327520743 hasConcept C138885662 @default.
- W4327520743 hasConcept C142724271 @default.
- W4327520743 hasConcept C148483581 @default.
- W4327520743 hasConcept C153180895 @default.
- W4327520743 hasConcept C154945302 @default.
- W4327520743 hasConcept C2776401178 @default.
- W4327520743 hasConcept C2779338263 @default.
- W4327520743 hasConcept C41008148 @default.
- W4327520743 hasConcept C41895202 @default.
- W4327520743 hasConcept C52622490 @default.
- W4327520743 hasConcept C71924100 @default.
- W4327520743 hasConcept C81363708 @default.
- W4327520743 hasConcept C95623464 @default.
- W4327520743 hasConceptScore W4327520743C108583219 @default.
- W4327520743 hasConceptScore W4327520743C119857082 @default.
- W4327520743 hasConceptScore W4327520743C138885662 @default.
- W4327520743 hasConceptScore W4327520743C142724271 @default.
- W4327520743 hasConceptScore W4327520743C148483581 @default.
- W4327520743 hasConceptScore W4327520743C153180895 @default.
- W4327520743 hasConceptScore W4327520743C154945302 @default.
- W4327520743 hasConceptScore W4327520743C2776401178 @default.
- W4327520743 hasConceptScore W4327520743C2779338263 @default.
- W4327520743 hasConceptScore W4327520743C41008148 @default.
- W4327520743 hasConceptScore W4327520743C41895202 @default.
- W4327520743 hasConceptScore W4327520743C52622490 @default.
- W4327520743 hasConceptScore W4327520743C71924100 @default.
- W4327520743 hasConceptScore W4327520743C81363708 @default.
- W4327520743 hasConceptScore W4327520743C95623464 @default.
- W4327520743 hasFunder F4320321001 @default.
- W4327520743 hasFunder F4320323086 @default.
- W4327520743 hasLocation W43275207431 @default.
- W4327520743 hasOpenAccess W4327520743 @default.
- W4327520743 hasPrimaryLocation W43275207431 @default.
- W4327520743 hasRelatedWork W2279398222 @default.
- W4327520743 hasRelatedWork W2732542196 @default.
- W4327520743 hasRelatedWork W2773120646 @default.
- W4327520743 hasRelatedWork W2986507176 @default.
- W4327520743 hasRelatedWork W2995914718 @default.
- W4327520743 hasRelatedWork W3011074480 @default.
- W4327520743 hasRelatedWork W3156786002 @default.
- W4327520743 hasRelatedWork W4299822940 @default.
- W4327520743 hasRelatedWork W4307883119 @default.
- W4327520743 hasRelatedWork W564581980 @default.
- W4327520743 isParatext "false" @default.
- W4327520743 isRetracted "false" @default.
- W4327520743 workType "article" @default.