Evidence map›Paper›PMID 38597451›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2024

[Automatic classification of immune-mediated glomerular diseases based on multi-modal multi-instance learning].

K Long, D Weng, J Geng, Y Lu, Z Zhou, L Cao

Open access · greenAbstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed, 0 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

K LongSchool of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Provincial Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
D WengSchool of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Provincial Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
J GengSchool of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Y LuCentral Laboratory, Southern Medical University, Guangzhou 510515, China.
Z ZhouCentral Laboratory, Southern Medical University, Guangzhou 510515, China.
L CaoSchool of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Provincial Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
Southern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo 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).

methodsWe 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.

resultsThe 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)%].

conclusionThis 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.

Indexed as

Glomerulonephritis, IGAHumansLevamisoleMicroscopy, Electron, TransmissionMicroscopy, FluorescenceRetrospective Studies3-aminolevamisoleLevamisoledeep learningglomerular diseasemulti-instance learningmulti-modal fusionrenal biopsy pathology

Identifiers

PMID38597451
PMCPMC11006708
OpenAlexW4394753162

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Textmetadata
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.