Abstract
Evidence-based medicine has received increasing attention. This type of medicine would have the benefit of using large data sets to investigate clinical–laboratory associations and validate hypotheses grounded on data. Pathology is one area that has been benefited from large data sets of images, having advances leveraged by computational pathology, which in turn relies in the advances of the methods conceived by the computational intelligence and the computer vision fields. This type of medicine would benefit of using large. By particularly considering kidney biopsies, computational nephropathology seeks to identify renal lesions from primary computer vision tasks that involve classification and segmentation of renal structures on histology images. In this context, this chapter aims at discussing some advances in computational nephropathology, contextualizing them in the scope of the PathoSpotter project. We also address current achievements and challenges, as well as dig in future prospects to the field.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
The Brazilian Health Ministry Research Agency. https://portal.fiocruz.br/en/
- 3.
References
Tolles WE. Section of biology: the cytoanalyzer—an example of physics in medical research. Trans N Y Acad Sci. 1955;17(3 Series II):250–6.
Abel J, Ouillette P, Williams C, Blau J, Cheng J, Yao K, Lee W, Cornish T, Balis U, McClintock D. Display characteristics and their impact on digital pathology: a current review of pathologists future microscope. J Pathol Inform. 2020;11:23. https://doi.org/10.4103/jpi.jpi_38_20.
Jader G, Fontineli J, Ruiz M, Abdalla K, Pithon M, Oliveira L. Deep instance segmentation of teeth in panoramic X-ray images. In: 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). Piscataway, NJ: IEEE; 2018. p. 400–7.
Silva G, Oliveira L, Pithon M. Automatic segmenting teeth in X-ray images: trends, a novel data set, benchmarking and future perspectives. Expert Syst Appl. 2018;107:15–31.
Silva B, Pinheiro L, Oliveira L, Pithon M. A study on tooth segmentation and numbering using end-to-end deep neural networks. In: 2020 33rd SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). Piscataway, NJ: IEEE; 2020. p. 164–71.
Abhishek D, Aarti S, Sachi G, Sudip D. Use of artificial intelligence in dermatology. Indian J Dermatol. 2020;65:352–7.
Abraham A, Sobhanakumari K, Mohan A. Artificial intelligence in dermatology. J Skin Sex Transm Dis. 2021;3(1):99–102.
Young AT, Xiong M, Pfau J, Keiser MJ, Wei ML. Artificial intelligence in dermatology: a primer. J Invest Dermatol. 2020;140(3):1504–12.
Barros GO, Navarro B, Duarte A, dos Santos WLC. PathoSpotter-K: a computational tool for the automatic identification of glomerular lesions in histological images of kidneys. Sci Rep. 2017;7:1–8. https://doi.org/10.1038/srep46769.
Ginley B, Tomaszewski JE, Yacoub R, Chen F, Sarder P. Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology. J Med Imaging. 2017;4(2):021102. https://doi.org/10.1117/1.jmi.4.2.021102.
Chagas P, Souza L, Araújo I, Aldeman N, Duarte A, Angelo M, dos Santos WLC, Oliveira L. Classification of glomerular hypercellularity using convolutional features and support vector machine. Artif Intell Med. 2020;103:101808. https://doi.org/10.1016/j.artmed.2020.101808.
Chagas P, Souza L, Calumby R, Duarte A, Angelo M, dos Santos WLC, Oliveira L. Deep-learningbased membranous nephropathy classification and Monte-Carlo dropout uncertainty estimation. In: Simpósio Brasileiro de Computação Aplicada à Saúde 𝑆𝐵𝐶𝐴𝑆; 2021. https://doi.org/10.5753/sbcas.2021.
Chagas P, Souza L, Calumby R, Pontes I, Araújo S, Duarte A, Pinheiro N, dos Santos WLC, Oliveira L. Toward unbounded open-set recognition to say “I don’t know” for glomerular multi-lesion classification. In: International symposium on medical information processing and analysis (SIPAIM); 2021.
Rehem JMC, Santos WLC, Duarte AA, Oliveira LR, Angelo MF. Automatic glomerulus detection in renal histological images. In: Proceedings–SPIE medical imaging 11603; 2021. https://doi.org/10.1117/12.2582201.
Tan P-N, Steinbach M, Karpatne A, Kumar V. Introduction to data mining. London: Pearson; 2019.
Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86:2278–323. https://doi.org/10.1109/5.726791.
Rajaraman A, Ullman JD. Mining of massive datasets. Cambridge: Cambridge University Press; 2011. https://doi.org/10.1017/CBO9781139058452.
Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge, MA: MIT Press; 2016. http://www.deeplearningbook.org.
Krizhevsky ISA, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90. https://doi.org/10.1145/3065386.
Russakovsky O, Deng J, Hao S, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211–52. https://doi.org/10.1007/s11263-015-0816-y.
Pratt LY, Pratt LY, Hanson SJ, Giles CL, Cowan JD. Discriminability-based transfer between neural networks. In: Advances in neural information processing systems; 1993. p. 204–11.
Prewitt JMS, Mendelsohn ML. The analysis of cell images. Ann N Y Acad Sci. 1966;128(3):1035–53. https://doi.org/10.1111/j.1749-6632.1965.tb11715.x.
Hekler A, Utikal JS, Enk AH, Solass W, Schmitt M, Klode J, Schadendorf D, Wiebke S, Franklin C, Bestvater F, Flaig MJ, Krahl D, von Kalle C, Fröhling S, Brinker TJ. Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur J Cancer. 2019;118:91–6. https://doi.org/10.1016/j.ejca.2019.06.012.
Arvaniti E, Fricker KS, Moret M, Rupp N, Hermanns T, Fankhauser C, Wey N, Wild PJ, Rüschoff JH, Claassen M. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep. 2018;8(12054):1–11. https://doi.org/10.1038/s41598-018-30535-1.
Fogo AB, Lusco MA, Najafian B, Alpers CE. AJKD atlas of renal pathology: minimal change disease. Am J Kidney Dis. 2015;66(2):376–7. https://doi.org/10.1053/j.ajkd.2015.04.006.
Fogo AB, Lusco MA, Najafian B, Alpers CE. AJKD atlas of renal pathology: focal segmental glomerulosclerosis. Am J Kidney Dis. 2015;66(2):e1–2. https://doi.org/10.1053/j.ajkd.2015.04.007.
Bajema IM, Wilhelmus S, Alpers CE, Bruijn JA, Colvin RB, Terencecook H, D’Agati VD, Ferrario F, Haas M, Jennette JC, Joh K, Nast CC, Noël LH, Rijnink EC, Roberts ISD, Seshan SV, Sethi S, Fogo AB. Revision of the International Society of Nephrology/Renal Pathology Society classification for lupus nephritis: clarification of definitions, and modified National Institutes of Health activity and chronicity indices. Kidney Int. 2018;93(4):789–96. https://doi.org/10.1016/j.kint.2017.11.023.
Bhowmik DM, Dinda AK, Mahanta P, Agarwal SK. The evolution of the Banff classification schema for diagnosing renal allograft rejection and its implications for clinicians. Indian J Nephrol. 2010;20(1):2–8. https://doi.org/10.4103/0971-4065.6208.
dos Santos WLC, Sweet GMM, Azevêdo LG, Tavares MB, Soares MFS, de Melo CVB, Carneiro MFM, de Souza Santos RF, Conrado MC, Braga DTL, Bessa MC, de Freitas Pinheiro Junior N, Bahiense-Oliveira M. Current distribution pattern of biopsy-proven glomerular disease in Salvador, Brazil, 40 years after an initial assessment. J Bras Nefrol. 2017;39(4):376–83. https://doi.org/10.5935/0101-2800.20170069.
Zhou XS, Huang TS. CBIR: from low-level features to high-level semantics. In: Image and video communications and processing, vol. 3974. Bellingham: International Society for Optics and Photonics; 2000. p. 426–31.
Mikolajczyk K, Schmid C. Scale & affine invariant interest point detectors. Int J Comput Vis. 2004;60(1):63–86.
Bay H, Ess A, Tuytelaars T, Van Gool L. Speededup robust features (SURF). Comput Vis Image Underst. 2008;110(3):346–59.
Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol. 1; 2005. p. 886–93.
Rinaldi AM, Russo C. A content based image retrieval approach based on multiple multimedia features descriptors in E-health environment. In: 2020 IEEE international symposium on medical measurements and applications (MeMeA); 2020. p. 1–6.
LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International symposium on circuits and systems. Piscataway, NJ: IEEE; 2010. p. 253–6.
Shah A, Naseem R, Iqbal S, Shah MA, et al. Improving cbir accuracy using convolutional neural network for feature extraction. In: 2017 13th international conference on emerging technologies (ICET). Piscataway, NJ: IEEE; 2017. p. 1–5.
Azizpour H, Razavian AS, Sullivan J, Maki A, Carlsson S. From generic to specific deep representations for visual recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops; 2015. p. 36–45.
Kruthika KR, Maheshappa HD, Initiative A’s DN, et al. CBIR system using capsule networks and 3D CNN for Alzheimer’s disease diagnosis. Inform Med Unlock. 2019;14:59–68.
Sezavar A, Farsi H, Mohamadzadeh S. Content-based image retrieval by combining convolutional neural networks and sparse representation. Multimed Tools Appl. 2019;78(15):20895–912.
Ozen Y, Aksoy S, Kösemehmetoğlu K, Önder S, Üner A. Self-supervised learning with graph neural networks for region of interest retrieval in histopathology. In: 2020 25th International conference on pattern recognition (ICPR); 2021. p. 6329–34. https://doi.org/10.1109/ICPR48806.2021.9412903.
Zheng Y, Jiang Z, Xie F, Shi J, Zhang H, Huai J, Cao M, Yang X. Diagnostic regions attention network (DRA-net) for histopathology WSI recommendation and retrieval. IEEE Trans Med Imaging. 2021;40(3):1090–103.
Hein M, Andriushchenko M, Bitterwolf J. Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2019. p. 41–50.
Kendall A, Gal Y. What uncertainties do we need in Bayesian deep learning for computer vision? In: Proceedings of the 31st international conference on neural information processing systems. NIPS’17. Long Beach, CA: Curran Associates; 2017. p. 5580–90.
Begoli E, Bhattacharya T, Kusnezov D. The need for uncertainty quantification in machine-assisted medical decision making. Nat Mach Intell. 2019;1(1):20–3.
Leibig C, Allken V, Ayhan MS, Berens P, Wahl S. Leveraging uncertainty information from deep neural networks for disease detection. Sci Rep. 2017;7(1):1–14.
Combalia M, Hueto F, Puig S, Malvehy J, Vilaplana V. Uncertainty estimation in deep neural networks for dermoscopic image classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops; 2020. p. 744–5.
Cicalese PA, Mobiny A, Shahmoradi Z, Yi X, Mohan C, Van Nguyen H. Kidney level lupus nephritis classification using uncertainty guided Bayesian convolutional neural net-works. IEEE J Biomed Health Inform. 2020;25(2):315–24.
Roady R, Hayes TL, Kemker R, Gonzales A, Kanan C. Are open set classification methods effective on large-scale datasets? PLoS One. 2020;15(9):e0238302.
Ginley B, Lutnick B, Jen KY, Fogo A, Jain S, Rosenberg A, Walavalkar V, Wilding G, Tomaszewski JE, Yacoub R, Rossi GM, Sarder P. Computational segmentation and classification of diabetic glomerulosclerosis. J Am Soc Nephrol. 2019;30(10):1953–67.
D’Agati VD, Fogo AB, Bruijn JA, Charles Jennette J. Pathologic classification of focal segmental glomerulosclerosis: a working proposal. Am J Kidney Dis. 2004;43(2):368–82. https://doi.org/10.1053/j.ajkd.2003.10.024.
Markowitz G. Updated Oxford classification of IgA nephropathy: a new MEST-C score. Nat Rev Nephrol. 2017;13(7):385–6.
Roberts ISD, Cook HT, Troyanov S, Alpers CE, Amore A, Barratt J, Berthoux F, Bonsib S, Bruijn JA, Cattran DC, Coppo R, D’Agati V, D’Amico G, Emancipator S, Emma F, Feehally J, Ferrario F, Fervenza SFFC, Geddes Agnes Fogo CC, Groene H-J, Andrew M, Haas HM, Hill PA, Hogg RJ, Hsu SI, Jennette JC, Joh K, Julian BA, Kawamura T, Lai FM, Li L-S, Li PKT, Liu Z-H, Mackinnon B, Mezzano S, Schena FP, Tomino Y, Walker HWPD, Weening JJ, Yoshikawa N, Zhang H. The Oxford classification of IgA nephropathy: pathology definitions, correlations, and reproducibility. Kidney Int. 2009;76(5):546–56. https://doi.org/10.1038/ki.2009.168.
Polito MG, De Moura LAR, Kirsztajn GM. An overview on frequency of renal biopsy diagnosis in Brazil: clinical and pathological patterns based on 9617 native kidney biopsies. Nephrol Dial Transplant. 2010;25:490–6. https://doi.org/10.1093/ndt/gfp355.
Alturkistani HA, Tashkandi FM, Mohammedsaleh ZM. Histological stains: a literature review and case study. In: Global journal of health science. Richmond Hill, ON: Canadian Center of Science and Education; 2015. p. 72–9. https://doi.org/10.5539/gjhs.v8n3p72.
Acknowledgments
The PathoSpotter project is partially sponsored by the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB), grants TO-P0008/15 and TO-SUS0031/2018, and by the Inova FIOCRUZ grant. Washington dos Santos and Luciano Oliveira are research fellows of Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), grants 306779/2017 and 307550/2018-4, respectively.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Oliveira, L. et al. (2022). PathoSpotter: Computational Intelligence Applied to Nephropathology. In: Bezerra da Silva Junior, G., Nangaku, M. (eds) Innovations in Nephrology. Springer, Cham. https://doi.org/10.1007/978-3-031-11570-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-11570-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11569-1
Online ISBN: 978-3-031-11570-7
eBook Packages: MedicineMedicine (R0)