Deep-learning-based membranous nephropathy classification and Monte-Carlo dropout uncertainty estimation
Resumo
Membranous Nephropathy (MN) is one of the most common glomerular diseases that cause adult nephrotic syndrome. To assist pathologists on MN classification, we evaluated three deep-learning-based architectures, namely, ResNet-18, DenseNet and Wide-ResNet. In addition, to accomplish more reliable results, we applied Monte-Carlo Dropout for uncertainty estimation. We achieved average F1-Scores above 92% for all models, with Wide-ResNet obtaining the highest average F1-Score (93.2%). For uncertainty estimation on Wide-ResNet, the uncertainty scores showed high relation with incorrect classifications, proving that these uncertainty estimates can support pathologists on the analysis of model predictions.
Referências
Murat Seckin Ayhan and Philipp Berens. Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks. In International Conference on Medical Imaging with Deep Learning, 2018.
Jan U Becker, David Mayerich, Meghana Padmanabhan, Jonathan Barratt, Angela Ernst, Peter Boor, Pietro A Cicalese, Chandra Mohan, Hien V Nguyen, and Badrinath Roysam. Articial intelligence and machine learning in nephropathology. Kidney International, 98(1):65–75, 2020.
Edmon Begoli, Tanmoy Bhattacharya, and Dimitri Kusnezov. The need for uncertainty quantication in machine-assisted medical decision making. Nature Machine Intelligence, 1(1):20–23, 2019.
Yilin Chen, Ming Li, Fang Hao, Weixia Han, Dan Niu, and Chen Wang. Classication of glomerular spikes using convolutional neural network. In Proceedings of the 2020 Conference on Articial Intelligence and Healthcare, pages 254–258, 2020.
Pietro A Cicalese, Aryan Mobiny, Zahed Shahmoradi, Xiongfeng Yi, Chandra Mohan, and Hien Van Nguyen. Kidney level lupus nephritis classication using uncertainty IEEE Journal of Biomedical and guided bayesian convolutional neural networks. Health Informatics, 2020.
Marc Combalia, Ferran Hueto, Susana Puig, Josep Malvehy, and Veronica Vilaplana. Uncertainty estimation in deep neural networks for dermoscopic image classication. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 744–745, 2020.
Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In Maria Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, pages 1050–1059, New York, New York, USA, 20–22 Jun 2016. PMLR.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for In Proceedings of the IEEE conference on computer vision and image recognition. pattern recognition, pages 770–778, 2016.
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017.
Alex Krizhevsky. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997, 2014.
Max-Heinrich Laves, Sontje Ihler, and Tobias Ortmaier. Uncertainty quantication in computer-aided diagnosis: Make your model say “i don’t know“ for ambiguous cases. In International Conference on Medical Imaging with Deep Learning – Extended Abstract Track, London, United Kingdom, 08–10 Jul 2019.
Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, and Siegfried Wahl. Leveraging uncertainty information from deep neural networks for disease detection. Scientic reports, 7(1):1–14, 2017.
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez. A survey on deep learning in medical image analysis. Medical image analysis, 42:60–88, 2017.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019.
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio. Transfusion: Understanding transfer learning for medical imaging. arXiv preprint arXiv:1902.07208, 2019.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks In International Conference on Medical image for biomedical image segmentation. computing and computer-assisted intervention, pages 234–241. Springer, 2015.
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3): 211–252, 2015.
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016.
Mingxing Tan and Quoc Le. Efcientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, pages 6105–6114. PMLR, 2019.
Eiichiro Uchino, Kanata Suzuki, Noriaki Sato, Ryosuke Kojima, Yoshinori Tamada, Shusuke Hiragi, Hideki Yokoi, Nobuhiro Yugami, Sachiko Minamiguchi, Hironori Haga, et al. Classication of glomerular pathological ndings using deep learning and nephrologist–ai collective intelligence approach. International Journal of Medical Informatics, 141:104231, 2020.
Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016.
Jan U Becker, David Mayerich, Meghana Padmanabhan, Jonathan Barratt, Angela Ernst, Peter Boor, Pietro A Cicalese, Chandra Mohan, Hien V Nguyen, and Badrinath Roysam. Articial intelligence and machine learning in nephropathology. Kidney International, 98(1):65–75, 2020.
Edmon Begoli, Tanmoy Bhattacharya, and Dimitri Kusnezov. The need for uncertainty quantication in machine-assisted medical decision making. Nature Machine Intelligence, 1(1):20–23, 2019.
Yilin Chen, Ming Li, Fang Hao, Weixia Han, Dan Niu, and Chen Wang. Classication of glomerular spikes using convolutional neural network. In Proceedings of the 2020 Conference on Articial Intelligence and Healthcare, pages 254–258, 2020.
Pietro A Cicalese, Aryan Mobiny, Zahed Shahmoradi, Xiongfeng Yi, Chandra Mohan, and Hien Van Nguyen. Kidney level lupus nephritis classication using uncertainty IEEE Journal of Biomedical and guided bayesian convolutional neural networks. Health Informatics, 2020.
Marc Combalia, Ferran Hueto, Susana Puig, Josep Malvehy, and Veronica Vilaplana. Uncertainty estimation in deep neural networks for dermoscopic image classication. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 744–745, 2020.
Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In Maria Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, pages 1050–1059, New York, New York, USA, 20–22 Jun 2016. PMLR.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for In Proceedings of the IEEE conference on computer vision and image recognition. pattern recognition, pages 770–778, 2016.
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017.
Alex Krizhevsky. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997, 2014.
Max-Heinrich Laves, Sontje Ihler, and Tobias Ortmaier. Uncertainty quantication in computer-aided diagnosis: Make your model say “i don’t know“ for ambiguous cases. In International Conference on Medical Imaging with Deep Learning – Extended Abstract Track, London, United Kingdom, 08–10 Jul 2019.
Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, and Siegfried Wahl. Leveraging uncertainty information from deep neural networks for disease detection. Scientic reports, 7(1):1–14, 2017.
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez. A survey on deep learning in medical image analysis. Medical image analysis, 42:60–88, 2017.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019.
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio. Transfusion: Understanding transfer learning for medical imaging. arXiv preprint arXiv:1902.07208, 2019.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks In International Conference on Medical image for biomedical image segmentation. computing and computer-assisted intervention, pages 234–241. Springer, 2015.
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3): 211–252, 2015.
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016.
Mingxing Tan and Quoc Le. Efcientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, pages 6105–6114. PMLR, 2019.
Eiichiro Uchino, Kanata Suzuki, Noriaki Sato, Ryosuke Kojima, Yoshinori Tamada, Shusuke Hiragi, Hideki Yokoi, Nobuhiro Yugami, Sachiko Minamiguchi, Hironori Haga, et al. Classication of glomerular pathological ndings using deep learning and nephrologist–ai collective intelligence approach. International Journal of Medical Informatics, 141:104231, 2020.
Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016.
Publicado
15/06/2021
Como Citar
CHAGAS, Paulo; SOUZA, Luiz; PONTES, Izabelle; CALUMBY, Rodrigo; ANGELO, Michele; DUARTE, Angelo; DOS-SANTOS, Washington L. C.; OLIVEIRA, Luciano.
Deep-learning-based membranous nephropathy classification and Monte-Carlo dropout uncertainty estimation. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 257-268.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2021.16070.
