Classification of Glomeruli with Membranous Nephropathy on Renal Digital Pathological Images with Deep Learning

Hao, Fang; Li, Ming; Liu, Xueyu; Li, Xinyu; Yue, Junhong; Han, Weixia

doi:10.1145/3433996.3434486

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…We here demonstrated this particularly challenging task can also be tackled by trained machine learning models with high degrees of accuracy. On this topic, only a handful of studies are present, and the lesions analyzed are limited [ 20 , [27] , [28] , [29] , [30] ]. In contrast, our study evaluated six major glomerular lesions required for proper classification of lupus nephritis.…”

Section: Discussionmentioning

confidence: 99%

“…In this regard, computer-aided image analysis may be helpful in establishing a better diagnostic workflow [ 26 ]. However, studies on identifying glomerular lesions using computer-aided image analysis techniques are scarce, and they focused on only one or few pathological features such as global sclerosis, hypercellularity, or glomerular capillary loop thickening [ 20 , [27] , [28] , [29] , [30] ]. Furthermore, a model distinguishing different glomerular disease based on light microscopic morphology has not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Glomerular disease classification and lesion identification by machine learning

Yang¹,

Lee²,

Wang

et al. 2022

Biomedical Journal

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Glomerular disease classification and lesion identification by machine learning

Yang¹,

Lee²,

Wang

et al. 2022

Biomedical Journal

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“…With the advent of deep learning, some scholars have proposed a multi-stage network architecture. Hao et al [26] proposed a CNN-based two stage network called MN-Net. The network initially employs a detection model to locate glomeruli in whole slide images and subsequently employs the classification module to classify glomerular diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al [27] also proposed a multi-stage model. In contrast to the MN-Net [26] proposed by Hao et al, this model incorporates a lesion identification component to identify glomerular lesions relevant to the disease.…”

Section: Introductionmentioning

confidence: 99%

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Self Reinforcing Multi-Class Transformer for Kidney Glomerular Basement Membrane Segmentation

Yan,

Huang,

Lian

et al. 2023

IEEE Access

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The precise segmentation of the glomerular basement membrane (GBM) can aid pathologists in making accurate pathological diagnoses. However, conventional methods solely focus on segmenting GBM from the background, disregarding the interconnections between GBM and its similar surrounding tissues, which leads to imprecise boundary segmentation of GBM. To address this issue, we employed a multi-category segmentation method to model the distinctions and interconnections between GBM and its similar surrounding tissues. Our experimental results demonstrate that this method can more accurately segment GBM with blurred boundaries. Historically, scholars have primarily used convolution to build models. This approach has limitation that only local information is modeled without effectively extracting global information. To address this issue, we propose a more reasonable structure combining convolution and attention, which we call the Self-Reinforcing Attention Mechanism. Experimental results indicate that the addition of the attention mechanism can help the segmentation of GBM by yielding more continuous boundaries. Finally, we incorporate the feature maps of each layer of the model in the loss function, allowing the model to focus semantic information at varying scales while also providing control over the model's focus by adjusting the weight. Our experimental results demonstrated that the proposed method has higher performance and better generalization ability than the state-of-the-art approaches.

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