Automatic classification of immune-mediated glomerular diseases based on multi-modal multi-instance learning
10.12122/j.issn.1673-4254.2024.03.21
- VernacularTitle:基于多模态多示例学习的免疫介导性肾小球疾病自动分类方法
- Author:
Kaixing LONG
1
;
Danyi WENG
;
Jian GENG
;
Yanmeng LU
;
Zhitao ZHOU
;
Lei CAO
Author Information
1. 南方医科大学,生物医学工程学院//广东省医学图像处理重点实验室//广东省医学成像与诊断技术工程实验室,广东 广州 510515
- Keywords:
renal biopsy pathology;
glomerular disease;
deep learning;
multi-modal fusion;
multi-instance learning
- From:
Journal of Southern Medical University
2024;44(3):585-593
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a multi-modal deep learning method for automatic classification of immune-mediated glomerular diseases based on images of optical microscopy(OM),immunofluorescence microscopy(IM),and transmission electron microscopy(TEM).Methods We retrospectively collected the pathological images from 273 patients and constructed a multi-modal multi-instance model for classification of 3 immune-mediated glomerular diseases,namely immunoglobulin A nephropathy(IgAN),membranous nephropathy(MN),and lupus nephritis(LN).This model adopts an instance-level multi-instance learning(I-MIL)method to select the TEM images for multi-modal feature fusion with the OM images and IM images of the same patient.By comparing this model with unimodal and bimodal models,we explored different combinations of the 3 modalities and the optimal methods for modal feature fusion.Results The multi-modal multi-instance model combining OM,IM,and TEM images had a disease classification accuracy of(88.34±2.12)%,superior to that of the optimal unimodal model[(87.08±4.25)%]and that of the optimal bimodal model[(87.92±3.06)%].Conclusion This multi-modal multi-instance model based on OM,IM,and TEM images can achieve automatic classification of immune-mediated glomerular diseases with a good classification accuracy.
