A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images
Abstract Objective To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for classifying gastric adenocarcinoma differentiation on whole-slide images (WSIs) without manual annotation. Methods and materials A hybrid multi-ins...
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BMC
2025-06-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | https://doi.org/10.1186/s12938-025-01407-3 |
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| author | Mudan Zhang Xinhuan Sun Wuchao Li Yin Cao Chen Liu Guilan Tu Jian Wang Rongpin Wang |
| author_facet | Mudan Zhang Xinhuan Sun Wuchao Li Yin Cao Chen Liu Guilan Tu Jian Wang Rongpin Wang |
| author_sort | Mudan Zhang |
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| description | Abstract Objective To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for classifying gastric adenocarcinoma differentiation on whole-slide images (WSIs) without manual annotation. Methods and materials A hybrid multi-instance learning model is proposed based on the Transformer and the graph attention network, called TGMIL, to classify the differentiation of gastric adenocarcinoma. A total of 613 WSIs from patients with gastric adenocarcinoma were retrospectively collected from two different hospitals. According to the differentiation of gastric adenocarcinoma, the data were divided into four groups: normal group (n = 254), well differentiation group (n = 166), moderately differentiation group (n = 75), and poorly differentiation group (n = 118). The gold standard of differentiation classification was blindly established by two gastrointestinal pathologists. The WSIs were randomly split into a training dataset consisting of 494 images and a testing dataset consisting of 119 images. Within the training set, the WSI count of the normal, well, moderately, and poorly differential groups was 203, 131, 62, and 98 individuals, respectively. Within the test set, the corresponding WSI count was 51, 35, 13, and 20 individuals. Results The TGMIL model developed for the differential prediction task exhibited remarkable efficiency when considering sensitivity, specificity, and the area under the curve (AUC) values. We also conducted a comparative analysis to assess the efficiency of five other models, namely MIL, CLAM_SB, CLAM_MB, DSMIL, and TransMIL, in classifying the differentiation of gastric cancer. The TGMIL model achieved a sensitivity of 73.33% and a specificity of 91.11%, with an AUC value of 0.86. Conclusions The hybrid multi-instance learning model TGMIL could accurately classify the differentiation of gastric adenocarcinoma using WSI without the need for labor-intensive and time-consuming manual annotations, which will improve the efficiency and objectivity of diagnosis. |
| format | Article |
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| institution | Kabale University |
| issn | 1475-925X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
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| series | BioMedical Engineering OnLine |
| spelling | doaj-art-a899d3d1158f40cbb70593df78f34b192025-08-20T03:31:42ZengBMCBioMedical Engineering OnLine1475-925X2025-06-0124111410.1186/s12938-025-01407-3A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide imagesMudan Zhang0Xinhuan Sun1Wuchao Li2Yin Cao3Chen Liu4Guilan Tu5Jian Wang6Rongpin Wang7Department of Radiology, Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People’s HospitalDepartment of Radiology, Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People’s HospitalDepartment of Radiology, Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People’s HospitalDepartment of pathology, Guizhou Provincial People’s HospitalDepartment of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University)Laboratory department, Guizhou Provincial Center for Clinical LaboratoryDepartment of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University)Department of Radiology, Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People’s HospitalAbstract Objective To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for classifying gastric adenocarcinoma differentiation on whole-slide images (WSIs) without manual annotation. Methods and materials A hybrid multi-instance learning model is proposed based on the Transformer and the graph attention network, called TGMIL, to classify the differentiation of gastric adenocarcinoma. A total of 613 WSIs from patients with gastric adenocarcinoma were retrospectively collected from two different hospitals. According to the differentiation of gastric adenocarcinoma, the data were divided into four groups: normal group (n = 254), well differentiation group (n = 166), moderately differentiation group (n = 75), and poorly differentiation group (n = 118). The gold standard of differentiation classification was blindly established by two gastrointestinal pathologists. The WSIs were randomly split into a training dataset consisting of 494 images and a testing dataset consisting of 119 images. Within the training set, the WSI count of the normal, well, moderately, and poorly differential groups was 203, 131, 62, and 98 individuals, respectively. Within the test set, the corresponding WSI count was 51, 35, 13, and 20 individuals. Results The TGMIL model developed for the differential prediction task exhibited remarkable efficiency when considering sensitivity, specificity, and the area under the curve (AUC) values. We also conducted a comparative analysis to assess the efficiency of five other models, namely MIL, CLAM_SB, CLAM_MB, DSMIL, and TransMIL, in classifying the differentiation of gastric cancer. The TGMIL model achieved a sensitivity of 73.33% and a specificity of 91.11%, with an AUC value of 0.86. Conclusions The hybrid multi-instance learning model TGMIL could accurately classify the differentiation of gastric adenocarcinoma using WSI without the need for labor-intensive and time-consuming manual annotations, which will improve the efficiency and objectivity of diagnosis.https://doi.org/10.1186/s12938-025-01407-3Gastric adenocarcinomaMulti-instance learningDifferentiationWhole-slide imagesTGMIL |
| spellingShingle | Mudan Zhang Xinhuan Sun Wuchao Li Yin Cao Chen Liu Guilan Tu Jian Wang Rongpin Wang A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images BioMedical Engineering OnLine Gastric adenocarcinoma Multi-instance learning Differentiation Whole-slide images TGMIL |
| title | A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images |
| title_full | A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images |
| title_fullStr | A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images |
| title_full_unstemmed | A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images |
| title_short | A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images |
| title_sort | hybrid multi instance learning based identification of gastric adenocarcinoma differentiation on whole slide images |
| topic | Gastric adenocarcinoma Multi-instance learning Differentiation Whole-slide images TGMIL |
| url | https://doi.org/10.1186/s12938-025-01407-3 |
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