Research graph
References from KPIs 2024 challenge: Advancing glomerular segmentation from patch- to slide-level. Local targets link to admitted publications; unresolved targets remain external evidence.
Basics of kidney biopsy: A nephrologist’s perspective
10.4103/0971-4065.114462 · 2013 · External reference
Effects of dipeptidyl peptidase-4 inhibitor and angiotensin-converting enzyme inhibitor on experimental diabetic kidney disease
10.1016/j.labinv.2023.100305 · 2024 · External reference
Semantic segmentation framework for glomeruli detection and classification in kidney histological sections
10.3390/electronics9030503 · 2020 · External reference
From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
10.1109/tmi.2018.2867350 · 2018 · External reference
From detection of individual metastases to classification of lymph node status at the patient level: The CAMELYON17 challenge
10.1109/tmi.2018.2867350 · 2019 · External reference
QuPath: Open source software for digital pathology image analysis
10.1038/s41598-017-17204-5 · 2017 · External reference
Digital pathology evaluation in the multicenter Nephrotic Syndrome Study Network (NEPTUNE)
10.2215/cjn.08370812 · 2013 · External reference
Reproducibility of the NEPTUNE descriptor-based scoring system on whole-slide images and histologic and ultrastructural digital images
10.1038/modpathol.2016.58 · 2016 · External reference
Artificial intelligence and machine learning in nephropathology
10.1016/j.kint.2020.02.027 · 2020 · External reference
Structure instance segmentation in renal tissue: a case study on tubular immune cell detection
2018 · External reference
Unresolved reference
2017 · External reference
Whole slide image quality in digital pathology: Review and perspectives
10.1109/access.2022.3227437 · 2022 · External reference
Glomerulosclerosis identification in whole slide images using semantic segmentation
10.1016/j.cmpb.2019.105273 · 2020 · External reference
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
10.1038/s41591-021-01620-2 · 2022 · External reference
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
10.1038/s41591-021-01620-2 · 2022 · External reference
A robust deep learning method for WSI-level diseased glomeruli segmentation
2024 · External reference
Unresolved reference
2024 · External reference
Sam-glomeruli: Enhanced segment anything model for precise glomeruli segmentation
2024 · External reference
Global epidemiology of kidney cancer
10.1093/ndt/gfae036 · 2024 · External reference
Enhancing physician flexibility: Prompt-guided multi-class pathological segmentation for diverse outcomes
2024 · External reference
Unresolved reference
2023 · External reference
Omni-seg: A scale-aware dynamic network for renal pathological image segmentation
10.1109/tbme.2023.3260739 · 2023 · External reference
HATs: Hierarchical adaptive taxonomy segmentation for panoramic pathology image analysis
2024 · External reference
10.1109/cvpr52733.2024.01115
10.1109/cvpr52733.2024.01115 · External reference
Multi-scale fully convolutional network for gland segmentation using three-class classification
10.1016/j.neucom.2019.10.097 · 2020 · External reference
Unresolved reference
2020 · External reference
Stain specific standardization of whole-slide histopathological images
10.1109/tmi.2015.2476509 · 2016 · External reference
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
10.1001/jama.2017.14585 · 2017 · External reference
A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues
10.1016/j.compmedimag.2021.101865 · 2021 · External reference
Computational segmentation and classification of diabetic glomerulosclerosis
10.1681/asn.2018121259 · 2019 · External reference
Automatic computational labeling of glomerular textural boundaries
2017 · External reference
Building robust pathology image analyses with uncertainty quantification
10.1016/j.cmpb.2021.106291 · 2021 · External reference
Segnext: Rethinking convolutional attention design for semantic segmentation
10.52202/068431-0084 · 2022 · External reference
10.1109/cvpr.2016.90
10.1109/cvpr.2016.90 · External reference
The native kidney biopsy: update and evidence for best practice
10.2215/cjn.05750515 · 2016 · External reference
AC-UNet: a self-adaptive cropping approach for kidney pathology image segmentation
2024 · External reference
Unresolved reference
2021 · External reference
Unet 3+: A full-scale connected unet for medical image segmentation
2020 · External reference
Gonadal dysfunction in men with chronic kidney disease: clinical features, prognostic implications and therapeutic options
10.5301/jn.2011.8481 · 2012 · External reference
NnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
10.1038/s41592-020-01008-z · 2021 · External reference
Digital pathology and ensemble deep learning for kidney cancer diagnosis: Dartmouth kidney cancer histology dataset
10.3390/applbiosci4010008 · 2025 · External reference
Segmentation of human functional tissue units in support of a Human Reference Atlas
10.1038/s42003-023-04848-5 · 2023 · External reference
Instance segmentation for whole slide imaging: end-to-end or detect-then-segment
10.1117/1.jmi.8.1.014001 · 2021 · External reference
A deep learning-based approach for glomeruli instance segmentation from multistained renal biopsy pathologic images
10.1016/j.ajpath.2021.05.004 · 2021 · External reference
Mast cell quantification in normal peritoneum and during peritoneal dialysis treatment
10.5858/2006-130-1188-mcqinp · 2006 · External reference
Acute kidney injury
10.1097/ccm.0b013e318168c4a4 · 2008 · External reference
Unresolved reference
2026 · External reference
10.1109/iccv51070.2023.00371
10.1109/iccv51070.2023.00371 · External reference
Ensembled SegNeXt based glomeruli segmentation
2024 · External reference
A multi-organ nucleus segmentation challenge
10.1109/tmi.2019.2947628 · 2020 · External reference
A multi-organ nucleus segmentation challenge
10.1109/tmi.2019.2947628 · 2019 · External reference
A dataset and a technique for generalized nuclear segmentation for computational pathology
10.1109/tmi.2017.2677499 · 2017 · External reference
Animal models of regression/progression of kidney disease
2014 · External reference
Tubulointerstitial fibrosis can sensitize the kidney to subsequent glomerular injury
10.1016/j.kint.2017.04.010 · 2017 · External reference
10.1109/cvpr.2017.106
10.1109/cvpr.2017.106 · External reference
1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
10.1093/gigascience/giy065 · 2018 · External reference
A hybrid CNN-TransXNet approach for advanced glomerular segmentation in renal histology imaging
10.1007/s44196-024-00523-7 · 2024 · External reference
10.1109/cvpr52688.2022.01167
10.1109/cvpr52688.2022.01167 · External reference
Digital pathology: accurate technique for quantitative assessment of histological features in metabolic-associated fatty liver disease
10.1111/apt.16100 · 2021 · External reference
Cortical thickness: an early morphological marker of atherosclerotic renal disease
10.1046/j.1523-1755.2002.00167.x · 2002 · External reference
Computer aided analysis of prostate histopathology images to support a refined Gleason grading system
2017 · External reference
U-net: Convolutional networks for biomedical image segmentation
2015 · External reference
Glomeruli segmentation in whole-slide images: Is better local performance always better?
2024 · External reference
Glomerulus detection using segmentation neural networks
10.1007/s10278-022-00764-y · 2023 · External reference
Unresolved reference
2024 · External reference
Methods and open-source toolkit for analyzing and visualizing challenge results
10.1038/s41598-021-82017-6 · 2021 · External reference
Application of digital pathology and machine learning in the liver, kidney and lung diseases
10.1016/j.jpi.2022.100184 · 2023 · External reference
10.1007/978-3-030-01228-1_26
10.1007/978-3-030-01228-1_26 · External reference
SegFormer: Simple and efficient design for semantic segmentation with transformers
2021 · External reference
Unresolved reference
2022 · External reference
Application of visual transformer in renal image analysis
10.1186/s12938-024-01209-z · 2024 · External reference
Unresolved reference
2024 · External reference
Unresolved reference
2024 · External reference
Unresolved reference
2024 · External reference
10.1109/iccv.2019.00612
10.1109/iccv.2019.00612 · External reference
Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning
10.1002/path.5491 · 2020 · External reference
Unresolved reference
2017 · External reference
10.1109/cvpr46437.2021.00681
10.1109/cvpr46437.2021.00681 · External reference
Unet++: A nested u-net architecture for medical image segmentation
2018 · External reference
Cross-species data integration for enhanced layer segmentation in kidney pathology
2025 · External reference
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
10.1001/jama.2017.14585 · ExternalCitation · doi-reference
Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning
10.1002/path.5491 · ExternalCitation · doi-reference
10.1007/978-3-030-01228-1_26
10.1007/978-3-030-01228-1_26 · ExternalCitation · doi-reference
Glomerulus detection using segmentation neural networks
10.1007/s10278-022-00764-y · ExternalCitation · doi-reference
A hybrid CNN-TransXNet approach for advanced glomerular segmentation in renal histology imaging
10.1007/s44196-024-00523-7 · ExternalCitation · doi-reference
A deep learning-based approach for glomeruli instance segmentation from multistained renal biopsy pathologic images
10.1016/j.ajpath.2021.05.004 · ExternalCitation · doi-reference
Glomerulosclerosis identification in whole slide images using semantic segmentation
10.1016/j.cmpb.2019.105273 · ExternalCitation · doi-reference
Building robust pathology image analyses with uncertainty quantification
10.1016/j.cmpb.2021.106291 · ExternalCitation · doi-reference
A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues
10.1016/j.compmedimag.2021.101865 · ExternalCitation · doi-reference
Application of digital pathology and machine learning in the liver, kidney and lung diseases
10.1016/j.jpi.2022.100184 · ExternalCitation · doi-reference
Tubulointerstitial fibrosis can sensitize the kidney to subsequent glomerular injury
10.1016/j.kint.2017.04.010 · ExternalCitation · doi-reference
Artificial intelligence and machine learning in nephropathology
10.1016/j.kint.2020.02.027 · ExternalCitation · doi-reference
Effects of dipeptidyl peptidase-4 inhibitor and angiotensin-converting enzyme inhibitor on experimental diabetic kidney disease
10.1016/j.labinv.2023.100305 · ExternalCitation · doi-reference
Multi-scale fully convolutional network for gland segmentation using three-class classification
10.1016/j.neucom.2019.10.097 · ExternalCitation · doi-reference
Reproducibility of the NEPTUNE descriptor-based scoring system on whole-slide images and histologic and ultrastructural digital images
10.1038/modpathol.2016.58 · ExternalCitation · doi-reference
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
10.1038/s41591-021-01620-2 · ExternalCitation · doi-reference
NnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
10.1038/s41592-020-01008-z · ExternalCitation · doi-reference
QuPath: Open source software for digital pathology image analysis
10.1038/s41598-017-17204-5 · ExternalCitation · doi-reference
Methods and open-source toolkit for analyzing and visualizing challenge results
10.1038/s41598-021-82017-6 · ExternalCitation · doi-reference
Segmentation of human functional tissue units in support of a Human Reference Atlas
10.1038/s42003-023-04848-5 · ExternalCitation · doi-reference
Cortical thickness: an early morphological marker of atherosclerotic renal disease
10.1046/j.1523-1755.2002.00167.x · ExternalCitation · doi-reference
1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
10.1093/gigascience/giy065 · ExternalCitation · doi-reference
Global epidemiology of kidney cancer
10.1093/ndt/gfae036 · ExternalCitation · doi-reference
Acute kidney injury
10.1097/ccm.0b013e318168c4a4 · ExternalCitation · doi-reference
Whole slide image quality in digital pathology: Review and perspectives
10.1109/access.2022.3227437 · ExternalCitation · doi-reference
10.1109/cvpr.2016.90
10.1109/cvpr.2016.90 · ExternalCitation · doi-reference
10.1109/cvpr.2017.106
10.1109/cvpr.2017.106 · ExternalCitation · doi-reference
10.1109/cvpr46437.2021.00681
10.1109/cvpr46437.2021.00681 · ExternalCitation · doi-reference
10.1109/cvpr52688.2022.01167
10.1109/cvpr52688.2022.01167 · ExternalCitation · doi-reference
10.1109/cvpr52733.2024.01115
10.1109/cvpr52733.2024.01115 · ExternalCitation · doi-reference
10.1109/iccv.2019.00612
10.1109/iccv.2019.00612 · ExternalCitation · doi-reference
10.1109/iccv51070.2023.00371
10.1109/iccv51070.2023.00371 · ExternalCitation · doi-reference
Omni-seg: A scale-aware dynamic network for renal pathological image segmentation
10.1109/tbme.2023.3260739 · ExternalCitation · doi-reference
Stain specific standardization of whole-slide histopathological images
10.1109/tmi.2015.2476509 · ExternalCitation · doi-reference
A dataset and a technique for generalized nuclear segmentation for computational pathology
10.1109/tmi.2017.2677499 · ExternalCitation · doi-reference
From detection of individual metastases to classification of lymph node status at the patient level: The CAMELYON17 challenge
10.1109/tmi.2018.2867350 · ExternalCitation · doi-reference
A multi-organ nucleus segmentation challenge
10.1109/tmi.2019.2947628 · ExternalCitation · doi-reference
Digital pathology: accurate technique for quantitative assessment of histological features in metabolic-associated fatty liver disease
10.1111/apt.16100 · ExternalCitation · doi-reference
Instance segmentation for whole slide imaging: end-to-end or detect-then-segment
10.1117/1.jmi.8.1.014001 · ExternalCitation · doi-reference
Application of visual transformer in renal image analysis
10.1186/s12938-024-01209-z · ExternalCitation · doi-reference
Computational segmentation and classification of diabetic glomerulosclerosis
10.1681/asn.2018121259 · ExternalCitation · doi-reference
The native kidney biopsy: update and evidence for best practice
10.2215/cjn.05750515 · ExternalCitation · doi-reference
Digital pathology evaluation in the multicenter Nephrotic Syndrome Study Network (NEPTUNE)
10.2215/cjn.08370812 · ExternalCitation · doi-reference
Digital pathology and ensemble deep learning for kidney cancer diagnosis: Dartmouth kidney cancer histology dataset
10.3390/applbiosci4010008 · ExternalCitation · doi-reference
Semantic segmentation framework for glomeruli detection and classification in kidney histological sections
10.3390/electronics9030503 · ExternalCitation · doi-reference
Basics of kidney biopsy: A nephrologist’s perspective
10.4103/0971-4065.114462 · ExternalCitation · doi-reference
Segnext: Rethinking convolutional attention design for semantic segmentation
10.52202/068431-0084 · ExternalCitation · doi-reference
Gonadal dysfunction in men with chronic kidney disease: clinical features, prognostic implications and therapeutic options
10.5301/jn.2011.8481 · ExternalCitation · doi-reference
Mast cell quantification in normal peritoneum and during peritoneal dialysis treatment
10.5858/2006-130-1188-mcqinp · ExternalCitation · doi-reference
