ArticleInternational journal of environmental research and public health2021
Multi-Task Learning-Based Immunofluorescence Classification of Kidney Disease.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-powered renal pathology for glomerular disease classification: a systematic review and meta-analysis.Renal failure · 2026Pooled it
- Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy.Renal failure · 2025Article
- Artificial Intelligence in Nephrology: Clinical Applications and Challenges.Kidney medicine · 2025Review
- Machine learning-based diagnosis and prognosis of IgAN: A systematic review and meta-analysis.Heliyon · 2024Review
- Development of a multiple convolutional neural network-facilitated diagnostic screening program for immunofluorescence images of IgA nephropathy and idiopathic membranous nephropathy.Clinical kidney journal · 2023Article
- Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects.Journal of clinical medicine · 2022Review
Corrections and comments
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Authors and funding
16 authors.
Funding
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Abstract
Chronic kidney disease is one of the most important causes of mortality worldwide, but a shortage of nephrology pathologists has led to delays or errors in its diagnosis and treatment. Immunofluorescence (IF) images of patients with IgA nephropathy (IgAN), membranous nephropathy (MN), diabetic nephropathy (DN), and lupus nephritis (LN) were obtained from the General Hospital of Chinese PLA. The data were divided into training and test data. To simulate the inaccurate focus of the fluorescence microscope, the Gaussian method was employed to blur the IF images. We proposed a novel multi-task learning (MTL) method for image quality assessment, de-blurring, and disease classification tasks. A total of 1608 patients' IF images were included-1289 in the training set and 319 in the test set. For non-blurred IF images, the classification accuracy of the test set was 0.97, with an AUC of 1.000. For blurred IF images, the proposed MTL method had a higher accuracy (0.94 vs. 0.93,
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Registered trials
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