@article{nikolić_mioljević_filipović_radojević_škodrić_životić_2023, title={Clinical and pathohistological characteristics of lupus nephritis in pediatric and adult population}, journal={Medicinska istrazivanja}, publisher={Medicinska istrazivanja}, author={Nikolić, Gorana and Mioljević, Ana and Filipović, Isidora and Radojević-Škodrić, Sanja and Životić, Maja}, year={2023} }

@article{dasari_chakraborty_truong_mohan_2019,
title = {A Systematic Review of Interpathologist Agreement in Histologic Classification of Lupus Nephritis},
journal = {Kidney International Reports},
volume = {4},
number = {10},
pages = {1420-1425},
year = {2019},
issn = {2468-0249},
doi = {https://doi.org/10.1016/j.ekir.2019.06.011},
url = {https://www.sciencedirect.com/science/article/pii/S2468024919313920},
author = {Shobha Dasari and Ashish Chakraborty and Luan Truong and Chandra Mohan},
keywords = {chronic kidney disease, glomerulonephritis, inflammation, kidney biopsy, lupus},
abstract = {Introduction
Lupus nephritis (LN) is one of the most severe manifestations of systemic lupus erythematosus (SLE), resulting in increased morbidity and mortality. The gold standard for diagnosis of LN is a renal biopsy. Considering the importance of the biopsy in determining long-term prognostication and treatment decisions, it is crucial to assess renal histopathology with utmost accuracy and precision. This review represents a systematic search of published literature to estimate the degree of interpathologist reproducibility in current assessment of LN.
Methods
Using the PubMed and Google Scholar search engines, studies analyzing the agreement of 4 or more pathologists assessing LN slides using the ISN/Renal Pathology Society (RPS) classification, activity index, and chronicity index were selected for analysis in this systematic review.
Results
In reviewing 6 qualifying studies (those analyzing the agreement of 4 or more pathologists using the ISN/RPS classification, activity index, and chronicity index) for the assignment of ISN/RPS class was 0.325 (interquartile range [IQR] 0.2405–0.425), which is “poor.” The median interpathologist concordance values for the assigned activity index and chronicity index were “moderate”: 0.52 (IQR 0.51–0.69) and 0.49 (IQR 0.36–0.58), respectively.
Conclusion
Thus, the current scoring using the ISN/RPS classification system and activity and chronicity indices for LN exhibits poor interpathologist agreement, which limits its use in clinical practice. Given that this can have severe repercussions on a patient’s treatment and prognosis, efforts to update pathology assessment guidelines, objectively measurable biomarkers, and deep learning approaches are strongly warranted.}
}

@article{rajkomar_oren_chen_dai_hajaj_hardt_liu_liu_marcus_sun_et_al_2018, title={Scalable and accurate deep learning with electronic health records}, volume={1}, ISSN={2398-6352}, url={https://dx.doi.org/10.1038/s41746-018-0029-1}, DOI={10.1038/s41746-018-0029-1}, number={1}, journal={npj Digital Medicine}, publisher={npj Digital Medicine}, author={Rajkomar, Alvin and Oren, Eyal and Chen, Kai and Dai, Andrew M. and Hajaj, Nissan and Hardt, Michaela and Liu, Peter J. and Liu, Xiaobing and Marcus, Jake and Sun, Mimi and et al.}, year={2018} }

@article{miotto_wang_wang_jiang_dudley_2018, title={Deep learning for healthcare: review, opportunities and challenges}, volume={19}, ISSN={1467-5463}, url={https://dx.doi.org/10.1093/bib/bbx044}, DOI={10.1093/bib/bbx044}, number={6}, journal={Briefings in Bioinformatics}, publisher={Briefings in Bioinformatics}, author={Miotto, Riccardo and Wang, Fei and Wang, Shuang and Jiang, Xiaoqian and Dudley, Joel T}, year={2018}, pages={1236–1246} }


@INPROCEEDINGS{kang2023benchmarking,
  author={Kang, Mingu and Song, Heon and Park, Seonwook and Yoo, Donggeun and Pereira, Sérgio},
  booktitle={2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Benchmarking Self-Supervised Learning on Diverse Pathology Datasets}, 
  year={2023},
  volume={},
  number={},
  pages={3344-3354},
  keywords={Pathology;Image segmentation;Computer vision;Computational modeling;Microscopy;Self-supervised learning;Pattern recognition;Medical and biological vision;cell microscopy},
  doi={10.1109/CVPR52729.2023.00326}}


@inproceedings{elmo,
    title = "Deep Contextualized Word Representations",
    author = "Peters, Matthew E.  and
      Neumann, Mark  and
      Iyyer, Mohit  and
      Gardner, Matt  and
      Clark, Christopher  and
      Lee, Kenton  and
      Zettlemoyer, Luke",
    booktitle = "Human Language Technologies",
    year = "2018",
    publisher = "Association for Computational Linguistics",
}

@article{celard_iglesias_sorribes_fdez_romero_vieira_borrajo_2023, title={A survey on deep learning applied to medical images: from simple artificial neural networks to generative models}, volume={35}, ISSN={0941-0643}, DOI={10.1007/s00521-022-07953-4}, number={3}, journal={Neural Computing and Applications}, publisher={Neural Computing and Applications}, author={Celard, P. and Iglesias, E. L. and Sorribes-Fdez, J. M. and Romero, R. and Vieira, A. Seara and Borrajo, L.}, year={2023}, pages={2291–2323} }

@inproceedings{Shao2021TransMILTB,
  title={TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classication},
  author={Zhucheng Shao and Hao Bian and Yang Chen and Yifeng Wang and Jian Zhang and Xiangyang Ji and Yongbing Zhang},
  booktitle={Neural Information Processing Systems},
  year={2021},
  url={https://api.semanticscholar.org/CorpusID:235294100}
}

@article{dosovitskiy2020image,
  title={An image is worth 16x16 words: Transformers for image recognition at scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others},
  journal={arXiv preprint arXiv:2010.11929},
  year={2020}
}

@article{nag_baidya_mandal_mathew_das_devi_kumar_2022, title={Deep learning tools for advancing drug discovery and development}, journal={3 Biotech}, publisher={3 Biotech}, author={Nag, Sagorika and Baidya, Anurag T. K. and Mandal, Abhimanyu and Mathew, Alen T. and Das, Bhanuranjan and Devi, Bharti and Kumar, Rajnish}, year={2022} }

@article{weening2004classification,
title = {The classification of glomerulonephritis in systemic lupus erythematosus revisited},
journal = {Kidney International},
volume = {65},
number = {2},
pages = {521-530},
year = {2004},
issn = {0085-2538},
doi = {https://doi.org/10.1111/j.1523-1755.2004.00443.x},
url = {https://www.sciencedirect.com/science/article/pii/S0085253815497340},
author = {Jan J. Weening and Vivette D. D'agati and Melvin M. Schwartz and Surya V. Seshan and Charles E. Alpers and Gerald B. Appel and James E. Balow and J.A.N. A. Bruijn and Terence Cook and Franco Ferrario and Agnes B. Fogo and Ellen M. Ginzler and Lee Hebert and Gary Hill and Prue Hill and J. Charles Jennette and Norella C. Kong and Philippe Lesavre and Michael Lockshin and Lai-Meng Looi and Hirofumi Makino and Luiz A. Moura and Michio Nagata and  {ON Behalf of the International Society of Nephrology and Renal Pathology Society Working Group on the Classification Oflupus Nephritis}},
keywords = {lupus erythematosus, classification, glomerulonephritis},
abstract = {The classification of glomerulonephritis in systemic lupus erythematosus revisited.The currently used classification reflects our understanding of the pathogenesis of the various forms of lupus nephritis, but clinicopathologic studies have revealed the need for improved categorization and terminology. Based on the 1982 classification published under the auspices of the World Health Organization (WHO) and subsequent clinicopathologic data, we propose that class I and II be used for purely mesangial involvement (I, mesangial immune deposits without mesangial hypercellularity; II, mesangial immune deposits with mesangial hypercellularity); class III for focal glomerulonephritis (involving <50% of total number of glomeruli) with subdivisions for active and sclerotic lesions; class IV for diffuse glomerulonephritis (involving ≥50% of total number of glomeruli) either with segmental (class IV-S) or global (class IV-G) involvement, and also with subdivisions for active and sclerotic lesions; class V for membranous lupus nephritis; and class VI for advanced sclerosing lesions. Combinations of membranous and proliferative glomerulonephritis (i.e., class III and V or class IV and V) should be reported individually in the diagnostic line. The diagnosis should also include entries for any concomitant vascular or tubulointerstitial lesions. One of the main advantages of the current revised classification is that it provides a clear and unequivocal description of the various lesions and classes of lupus nephritis, allowing a better standardization and lending a basis for further clinicopathologic studies. We hope that this revision, which evolved under the auspices of the International Society of Nephrology and the Renal Pathology Society, will contribute to further advancement of the WHO classification.}
}
@article{ginley2019computational,
  title={Computational segmentation and classification of diabetic glomerulosclerosis},
  author={Ginley, Brandon and Lutnick, Brendon and Jen, Kuang-Yu and Fogo, Agnes B and Jain, Sanjay and Rosenberg, Avi and Walavalkar, Vighnesh and Wilding, Gregory and Tomaszewski, John E and Yacoub, Rabi and others},
  journal={Journal of the American Society of Nephrology: JASN},
  year={2019},
  publisher={American Society of Nephrology}
}

@article{kang2021stainnet,
  title={Stainnet: a fast and robust stain normalization network},
  author={Kang, Hongtao and Luo, Die and Feng, Weihua and Zeng, Shaoqun and Quan, Tingwei and Hu, Junbo and Liu, Xiuli},
  journal={Frontiers in Medicine},
  volume={8},
  pages={746307},
  year={2021},
  publisher={Frontiers Media SA}
}

@article{vamathevan_clark_czodrowski_dunham_ferran_lee_li_madabhushi_shah_spitzer_et_al_2019, title={Applications of machine learning in drug discovery and development}, volume={18}, ISSN={1474-1776}, url={https://dx.doi.org/10.1038/s41573-019-0024-5}, DOI={10.1038/s41573-019-0024-5}, number={6}, journal={Nature Reviews Drug Discovery}, publisher={Nature Reviews Drug Discovery}, author={Vamathevan, Jessica and Clark, Dominic and Czodrowski, Paul and Dunham, Ian and Ferran, Edgardo and Lee, George and Li, Bin and Madabhushi, Anant and Shah, Parantu and Spitzer, Michaela and et al.}, year={2019}, pages={463–477} }

@inproceedings{li2021dual,
  title={Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning},
  author={Li, Bin and Li, Yin and Eliceiri, Kevin W},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={14318--14328},
  year={2021}
}

@article{WU2023100184,
title = {Application of digital pathology and machine learning in the liver, kidney and lung diseases},
journal = {Journal of Pathology Informatics},
volume = {14},
pages = {100184},
year = {2023},
issn = {2153-3539},
doi = {https://doi.org/10.1016/j.jpi.2022.100184},
url = {https://www.sciencedirect.com/science/article/pii/S2153353922007842},
author = {Benjamin Wu and Gilbert Moeckel},
keywords = {Digital pathology, Machine learning (ML), Whole slide imaging (WSI), Liver, Kidney, Lung},
abstract = {The development of rapid and accurate Whole Slide Imaging (WSI) has paved the way for the application of Artificial Intelligence (AI) to digital pathology. The availability of WSI in the recent years allowed the rapid development of various AI technologies to blossom. WSI-based digital pathology combined with neural networks can automate arduous and time-consuming tasks of slide evaluation. Machine Learning (ML)-based AI has been demonstrated to outperform pathologists by eliminating inter- and intra-observer subjectivity, obtaining quantitative data from slide images, and extracting hidden image patterns that are relevant to disease subtype and progression. In this review, we outline the functionality of different AI technologies such as neural networks and deep learning and discover how aspects of different diseases make them benefit from the implementation of AI. AI has proven to be valuable in many different organs, with this review focusing on the liver, kidney, and lungs. We also discuss how AI and image analysis not only can grade diseases objectively but also discover aspects of diseases that have prognostic value. In the end, we review the current status of the integration of AI in pathology and share our vision on the future of digital pathology.}
}


@article{wu_cheng_huang_pei_zuo_liu_yang_zhu_zhang_hong_et_al_2022, title={Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications}, volume={14}, ISSN={2072-6694}, DOI={10.3390/cancers14051199}, number={5}, journal={Cancers}, publisher={Cancers}, author={Wu, Yawen and Cheng, Michael and Huang, Shuo and Pei, Zongxiang and Zuo, Yingli and Liu, Jianxin and Yang, Kai and Zhu, Qi and Zhang, Jie and Hong, Honghai and et al.}, year={2022}, pages={1199} }

@Article{ahmed_abouzid_kaczmarek_2022,
AUTHOR = {Ahmed, Alhassan Ali and Abouzid, Mohamed and Kaczmarek, Elżbieta},
TITLE = {Deep Learning Approaches in Histopathology},
JOURNAL = {Cancers},
VOLUME = {14},
YEAR = {2022},
NUMBER = {21},
ARTICLE-NUMBER = {5264},
URL = {https://www.mdpi.com/2072-6694/14/21/5264},
PubMedID = {36358683},
ISSN = {2072-6694},
ABSTRACT = {The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers’ routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology.},
DOI = {10.3390/cancers14215264}
}

@article{hermsen2019deep,
  title = {Deep Learning–Based Histopathologic Assessment of Kidney Tissue},
  author = {Hermsen, Maarten and Steenbergen, Erik J. and Kers, Jesper and Florquin, Sandrine and H. Roelofs, Jacob T. and Stegall, Mark D. and Alexander, Mark P. and Smith, Benjamin H. and Smeets, Bart and Hilbrands, Luuk B.},
  journal = {Journal of the American Society of Nephrology : JASN},
  year = {2019},
}

@article{sheehan_korstanje_2018,
author = {Sheehan, Susan M. and Korstanje, Ron},
title = {Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning},
journal = {American Journal of Physiology-Renal Physiology},
volume = {315},
number = {6},
pages = {F1644-F1651},
year = {2018},
doi = {10.1152/ajprenal.00629.2017},
    note ={PMID: 30256126},
URL = { https://doi.org/10.1152/ajprenal.00629.2017},
eprint = {https://doi.org/10.1152/ajprenal.00629.2017}
,abstract = { Current methods of scoring histological kidney samples, specifically glomeruli, do not allow for collection of quantitative data in a high-throughput and consistent manner. Neither untrained individuals nor computers are presently capable of identifying glomerular features, so expert pathologists must do the identification and score using a categorical matrix, complicating statistical analysis. Critical information regarding overall health and physiology is encoded in these samples. Rapid comprehensive histological scoring could be used, in combination with other physiological measures, to significantly advance renal research. Therefore, we used machine learning to develop a high-throughput method to automatically identify and collect quantitative data from glomeruli. Our method requires minimal human interaction between steps and provides quantifiable data independent of user bias. The method uses free existing software and is usable without extensive image analysis training. Validation of the classifier and feature scores in mice is highlighted in this work and shows the power of applying this method in murine research. Preliminary results indicate that the method can be applied to data sets from different species after training on relevant data, allowing for fast glomerular identification and quantitative measurements of glomerular features. Validation of the classifier and feature scores are highlighted in this work and show the power of applying this method. The resulting data are free from user bias. Continuous data, such that statistical analysis can be performed, allows for more precise and comprehensive interrogation of samples. These data can then be combined with other physiological data to broaden our overall understanding of renal function. }
}


@article{besusparis_morkunas_laurinavicius_2023, title={A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images}, journal={Journal of Imaging}, publisher={Journal of Imaging}, author={Besusparis, Justinas and Morkunas, Mindaugas and Laurinavicius, Arvydas}, year={2023}}


@article{zheng_cassol,
title = {Deep-Learning–Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies},
journal = {The American Journal of Pathology},
volume = {191},
number = {8},
pages = {1442-1453},
year = {2021},
issn = {0002-9440},
doi = {https://doi.org/10.1016/j.ajpath.2021.05.005},
url = {https://www.sciencedirect.com/science/article/pii/S000294402100208X},
author = {Yi Zheng and Clarissa A. Cassol and Saemi Jung and Divya Veerapaneni and Vipul C. Chitalia and Kevin Y.M. Ren and Shubha S. Bellur and Peter Boor and Laura M. Barisoni and Sushrut S. Waikar and Margrit Betke and Vijaya B. Kolachalama}
}

@article{islam2023chronic,
  author = {Islam, M. A. and Hasan Majumder, M. Z. and Hussein, M. A.},
  title = {Chronic Kidney Disease Prediction Based on Machine Learning Algorithms},
  journal = {Journal of Pathology Informatics},
  volume = {14},
  year = {2023},
  url = {https://doi.org/10.1016/j.jpi.2023.100189}
}

@article{pan_li_wang_zhou_wu_ren_liu_lv_su_tang_2021, title={Identification of Barrett's esophagus in endoscopic images using deep learning}, volume={21}, ISSN={1471-230X}, DOI={10.1186/s12876-021-02055-2}, number={1}, journal={BMC Gastroenterology}, publisher={BMC Gastroenterology}, author={Pan, Wen and Li, Xujia and Wang, Weijia and Zhou, Linjing and Wu, Jiali and Ren, Tao and Liu, Chao and Lv, Muhan and Su, Song and Tang, Yong}, year={2021} }

@article{wang_xu_wang_wang_leng_fu_liu_qin_huang_2023, title={Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis}, volume={72}, ISSN={1023-3830}, DOI={10.1007/s00011-023-01755-7}, number={6}, journal={Inflammation Research}, publisher={Inflammation Research}, author={Wang, Da-Cheng and Xu, Wang-Dong and Wang, Shen-Nan and Wang, Xiang and Leng, Wei and Fu, Lu and Liu, Xiao-Yan and Qin, Zhen and Huang, An-Fang}, year={2023}, pages={1315–1324} }

@inproceedings{10.1117/12.2548528,
author = {Brandon Ginley and Kuang-Yu Jen and Avi Rosenberg and Giovanni Maria Rossi and Sanjay Jain and Pinaki Sarder},
title = {{Fully automated classification of glomerular lesions in lupus nephritis}},
volume = {11320},
booktitle = {Medical Imaging 2020: Digital Pathology},
editor = {John E. Tomaszewski and Aaron D. Ward},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {113200Y},
keywords = {digital diagnosis, lupus nephritis, glomerulus, feature extraction, disease classification},
year = {2020},
doi = {10.1117/12.2548528},
URL = {https://doi.org/10.1117/12.2548528}
}


@Article{diagnostics11111983,
AUTHOR = {Zheng, Zhaohui and Zhang, Xiangsen and Ding, Jin and Zhang, Dingwen and Cui, Jihong and Fu, Xianghui and Han, Junwei and Zhu, Ping},
TITLE = {Deep Learning-Based Artificial Intelligence System for Automatic Assessment of Glomerular Pathological Findings in Lupus Nephritis},
JOURNAL = {Diagnostics},
VOLUME = {11},
YEAR = {2021},
NUMBER = {11},
ARTICLE-NUMBER = {1983},
URL = {https://www.mdpi.com/2075-4418/11/11/1983},
PubMedID = {34829330},
ISSN = {2075-4418},
ABSTRACT = {Accurate assessment of renal histopathology is crucial for the clinical management of patients with lupus nephritis (LN). However, the current classification system has poor interpathologist agreement. This paper proposes a deep convolutional neural network (CNN)-based system that detects and classifies glomerular pathological findings in LN. A dataset of 349 renal biopsy whole-slide images (WSIs) (163 patients with LN, periodic acid-Schiff stain, 3906 glomeruli) annotated by three expert nephropathologists was used. The CNN models YOLOv4 and VGG16 were employed to localise the glomeruli and classify glomerular lesions (slight/severe impairments or sclerotic lesions). An additional 321 unannotated WSIs from 161 patients were used for performance evaluation at the per-patient kidney level. The proposed model achieved an accuracy of 0.951 and Cohen’s kappa of 0.932 (95% CI 0.915–0.949) for the entire test set for classifying the glomerular lesions. For multiclass detection at the glomerular level, the mean average precision of the CNN was 0.807, with ‘slight’ and ‘severe’ glomerular lesions being easily identified (F1: 0.924 and 0.952, respectively). At the per-patient kidney level, the model achieved a high agreement with nephropathologist (linear weighted kappa: 0.855, 95% CI: 0.795–0.916, p < 0.001; quadratic weighted kappa: 0.906, 95% CI: 0.873–0.938, p < 0.001). The results suggest that deep learning is a feasible assistive tool for the objective and automatic assessment of pathological LN lesions.},
DOI = {10.3390/diagnostics11111983}
}

@INPROCEEDINGS{8073562,
  author={Temerinac-Ott, M. and Forestier, G. and Schmitz, J. and Hermsen, M. and Bräsen, J.H. and Feuerhake, F. and Wemmert, C.},
  booktitle={Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis}, 
  title={Detection of glomeruli in renal pathology by mutual comparison of multiple staining modalities}, 
  year={2017},
  volume={},
  number={},
  pages={19-24},
  doi={10.1109/ISPA.2017.8073562}}

@article{YANG2022675,
title = {Glomerular disease classification and lesion identification by machine learning},
journal = {Biomedical Journal},
volume = {45},
number = {4},
pages = {675-685},
year = {2022},
issn = {2319-4170},
doi = {https://doi.org/10.1016/j.bj.2021.08.011},
url = {https://www.sciencedirect.com/science/article/pii/S2319417021001116},
author = {Cheng-Kun Yang and Ching-Yi Lee and Hsiang-Sheng Wang and Shun-Chen Huang and Peir-In Liang and Jung-Sheng Chen and Chang-Fu Kuo and Kun-Hua Tu and Chao-Yuan Yeh and Tai-Di Chen},
keywords = {Machine learning, Deep learning, Kidney biopsy, Glomerulonephritis},
abstract = {Background
Classification of glomerular diseases and identification of glomerular lesions require careful morphological examination by experienced nephropathologists, which is labor-intensive, time-consuming, and prone to interobserver variability. In this regard, recent advance in machine learning-based image analysis is promising.
Methods
We combined Mask Region-based Convolutional Neural Networks (Mask R–CNN) with an additional classification step to build a glomerulus detection model using human kidney biopsy samples. A Long Short-Term Memory (LSTM) recurrent neural network was applied for glomerular disease classification, and another two-stage model using ResNeXt-101 was constructed for glomerular lesion identification in cases of lupus nephritis.
Results
The detection model showed state-of-the-art performance on variedly stained slides with F1 scores up to 0.944. The disease classification model showed good accuracies up to 0.940 on recognizing different glomerular diseases based on H&E whole slide images. The lesion identification model demonstrated high discriminating power with area under the receiver operating characteristic curve up to 0.947 for various glomerular lesions. Models showed good generalization on external testing datasets.
Conclusion
This study is the first-of-its-kind showing how each step of kidney biopsy interpretation carried out by nephropathologists can be captured and simulated by machine learning models. The models were integrated into a whole slide image viewing and annotating platform to enable nephropathologists to review, correct, and confirm the inference results. Further improvement on model performances and incorporating inputs from immunofluorescence, electron microscopy, and clinical data might realize actual clinical use.}
}


@ARTICLE{8809829,
  author={Tellez, David and Litjens, Geert and van der Laak, Jeroen and Ciompi, Francesco},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Neural Image Compression for Gigapixel Histopathology Image Analysis}, 
  year={2021},
  volume={43},
  number={2},
  pages={567-578},
  doi={10.1109/TPAMI.2019.2936841}}


@article{Campanella2019,
  author = {Campanella, Gabriele and Hanna, Matthew G. and Geneslaw, Luke and Miraflor, Allen and Werneck Krauss Silva, Vitor and Busam, Klaus J. and Brogi, Edi and Reuter, Victor E. and Klimstra, David S. and Fuchs, Thomas J.},
  title = {Clinical-grade computational pathology using weakly supervised deep learning on whole slide images},
  journal = {Nature Medicine},
  year = {2019},
}
@article{maron1997framework,
  title={A framework for multiple-instance learning},
  author={Maron, Oded and Lozano-P{\'e}rez, Tom{\'a}s},
  journal={Advances in neural information processing systems},
  volume={10},
  year={1997}
}

@inproceedings{ilse2018attention,
  title={Attention-based deep multiple instance learning},
  author={Ilse, Maximilian and Tomczak, Jakub and Welling, Max},
  booktitle={International conference on machine learning},
  pages={2127--2136},
  year={2018},
  organization={PMLR}
}

@article{pinckaers2020streaming,
  title={Streaming convolutional neural networks for end-to-end learning with multi-megapixel images},
  author={Pinckaers, Hans and Van Ginneken, Bram and Litjens, Geert},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={44},
  number={3},
  pages={1581--1590},
  year={2020},
  publisher={IEEE}
}

@article{segmentation1,
  author = {Bueno, G. and Fernandez-Carrobles, M. M. and Gonzalez-Lopez, L. and Deniz, O.},
  title = {Glomerulosclerosis identification in whole slide images using semantic segmentation},
  journal = {Computer Methods and Programs in Biomedicine},
  year = {2020},
  volume = {184},
  pages = {105273},
  doi = {10.1016/j.cmpb.2019.105273},
  url = {https://doi.org/10.1016/j.cmpb.2019.105273},
}

@ARTICLE{cicalese2020kidney,
  author={Cicalese, Pietro Antonio and Mobiny, Aryan and Shahmoradi, Zahed and Yi, Xiongfeng and Mohan, Chandra and Van Nguyen, Hien},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={Kidney Level Lupus Nephritis Classification Using Uncertainty Guided Bayesian Convolutional Neural Networks}, 
  year={2021},
  volume={25},
  number={2},
  pages={315-324},
  keywords={Uncertainty;Bayes methods;Solid modeling;Kidney;Diseases;Task analysis;Biopsy;Bayesian deep network;lupus glomerulonephritis;lupus nephritis;model uncertainty;physician-friendly machine learning},
  doi={10.1109/JBHI.2020.3039162}}

@article{YU2022102871,
title = {Lupus nephritis: new progress in diagnosis and treatment},
journal = {Journal of Autoimmunity},
volume = {132},
pages = {102871},
year = {2022},
issn = {0896-8411},
doi = {https://doi.org/10.1016/j.jaut.2022.102871},
url = {https://www.sciencedirect.com/science/article/pii/S0896841122000798},
author = {Chen Yu and Ping Li and Xin Dang and Xuan Zhang and Yonghui Mao and Xiangmei Chen},
keywords = {Lupus nephritis, Diagnosis, Treatment, Biologics},
abstract = {Systemic lupus erythematosus (SLE) is a chronic multifactorial autoimmune disease that affects many organs, including the kidney. Lupus nephritis (LN) is a common manifestation characterized by heterogeneous clinical and histopathological findings, and often associates with poor prognosis. The diagnosis and treatment of LN is challenging, depending largely on renal biopsy, and there is no reliable non-invasive LN biomarker. Up to now, the complete remission rate of LN is only 20%∼30% after receiving six months of standard treatment, which is far from satisfactory. Moreover, adverse reactions to immunosuppressants, especially glucocorticoids, further compromise the prognosis of LN. Biological reagents targetting autoimmune responses and inflammatory pathways, bring hope to the treatment of intractable lupus. The European Renal Association-European Dialysis and Transplant Association (EULAR/ERA-EDTA) and KDIGO (Kidney Disease: Improving Global Outcomes) have been working on and launched the recommendations for the management of LN. In this review, we update our knowledge in the pathogenesis, diagnosis, and management of LN and prospect for the future potential targets in the management of LN.}
}

@article{Lu2021,
  author = {Ming Y. Lu and Drew F. K. Williamson and Tiffany Y. Chen and Richard J. Chen and Matteo Barbieri and Faisal Mahmood},
  title = {Data-efficient and weakly supervised computational pathology on whole-slide images},
  journal = {Nature Biomedical Engineering},
  volume = {5},
  number = {6},
  pages = {555-570},
  year = {2021},
  month = {June},
  doi = {10.1038/s41551-020-00682-w},
  url = {https://doi.org/10.1038/s41551-020-00682-w},
}

@inproceedings{
dosovitskiy2021an,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=YicbFdNTTy}
}

@article{hemmatirad2023investigation,
  title={An Investigation into Glomeruli Detection in Kidney H\&E and PAS Images using YOLO},
  author={Hemmatirad, Kimia and Babaie, Morteza and Hodgin, Jeffrey and Pantanowitz, Liron and Tizhoosh, HR},
  journal={arXiv preprint arXiv:2307.13199},
  year={2023}
}