Luan V. C. Martins
ABSTRACT
Machine learning (ML) has emerged as a powerful tool for improving the clinical pathology routine; however, developing novel ML research requires a complex multidisciplinary team effort. Therefore, a software for tackling the challenges of effectively annotating, building, and validating ML models is desirable. In this work we present WSI2ML, a web-based platform that provides a friendly interface suited for ML computational pathology research to be easily performed. Compared to similar tools currently available, the proposed software provides a complete toolset for each stage of ML workflow. We demonstrate the usefulness and functionality of WSI2ML by analyzing the performance results obtained in a tissue recognition task using a novel gastric cancer research dataset that is currently being developed with the tool. The software, documentation, installation instructions and related annotation handling library is freely available at https://luanvcmartins.github.io/WSI2ML/.
- David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, and Lars Petersson. 2022. A survey on graph-based deep learning for computational histopathology. Computerized Medical Imaging and Graphics 95 (2022), 102027.Google Scholar
Cross Ref
- Peter Bankhead, Maurice B Loughrey, José A Fernández, Yvonne Dombrowski, Darragh G McArt, Philip D Dunne, Stephen McQuaid, Ronan T Gray, Liam J Murray, Helen G Coleman, 2017. QuPath: Open source software for digital pathology image analysis. Scientific reports 7, 1 (2017), 1–7.Google Scholar
- Howard Butler, Martin Daly, Allan Doyle, Sean Gillies, Stefan Hagen, and Tim Schaub. 2016. The geojson format. Technical Report.Google Scholar
- Mingyu Chen, Bin Zhang, Win Topatana, Jiasheng Cao, Hepan Zhu, Sarun Juengpanich, Qijiang Mao, Hong Yu, and Xiujun Cai. 2020. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ precision oncology 4, 1 (2020), 14.Google Scholar
- Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi, Gabriel Silva De Souza, Alexi Baidoshvili, Geert Litjens, Bram Van Ginneken, Iris Nagtegaal, and Jeroen Van Der Laak. 2017. The importance of stain normalization in colorectal tissue classification with convolutional networks. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, 160–163.Google ScholarCross Ref
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248–255.Google ScholarCross Ref
- Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, and Yan Xu. 2020. Deep learning in digital pathology image analysis: a survey. Frontiers of medicine 14 (2020), 470–487.Google Scholar
- Amelie Echle, Niklas Timon Rindtorff, Titus Josef Brinker, Tom Luedde, Alexander Thomas Pearson, and Jakob Nikolas Kather. 2021. Deep learning in cancer pathology: a new generation of clinical biomarkers. British journal of cancer 124, 4 (2021), 686–696.Google Scholar
- Adam Goode, Benjamin Gilbert, Jan Harkes, Drazen Jukic, and Mahadev Satyanarayanan. 2013. OpenSlide: A vendor-neutral software foundation for digital pathology. Journal of pathology informatics 4, 1 (2013), 27.Google ScholarCross Ref
- Matthew G Hanna, Anil Parwani, and Sahussapont Joseph Sirintrapun. 2020. Whole slide imaging: technology and applications. Advances in Anatomic Pathology 27, 4 (2020), 251–259.Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.Google ScholarCross Ref
- MP Humphries, P Maxwell, and M Salto-Tellez. 2021. QuPath: The global impact of an open source digital pathology system. Computational and Structural Biotechnology Journal 19 (2021), 852–859.Google ScholarCross Ref
- Cesare Lancellotti, Pierandrea Cancian, Victor Savevski, Soumya Rupa Reddy Kotha, Filippo Fraggetta, Paolo Graziano, and Luca Di Tommaso. 2021. Artificial intelligence & tissue biomarkers: advantages, risks and perspectives for pathology. Cells 10, 4 (2021), 787.Google ScholarCross Ref
- Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. 2022. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11976–11986.Google ScholarCross Ref
- Marc Macenko, Marc Niethammer, James S Marron, David Borland, John T Woosley, Xiaojun Guan, Charles Schmitt, and Nancy E Thomas. 2009. A method for normalizing histology slides for quantitative analysis. In 2009 IEEE international symposium on biomedical imaging: from nano to macro. IEEE, 1107–1110.Google ScholarCross Ref
- Raphaël Marée, Loïc Rollus, Benjamin Stévens, Renaud Hoyoux, Gilles Louppe, Rémy Vandaele, Jean-Michel Begon, Philipp Kainz, Pierre Geurts, and Louis Wehenkel. 2016. Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics 32, 9 (2016), 1395–1401.Google ScholarCross Ref
- Christian Marzahl, Marc Aubreville, Christof A Bertram, Jennifer Maier, Christian Bergler, Christine Kröger, Jörn Voigt, Katharina Breininger, Robert Klopfleisch, and Andreas Maier. 2021. EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control. Scientific reports 11, 1 (2021), 1–11.Google Scholar
- Sambit K Mohanty and Anil V Parwani. 2022. Whole slide imaging: applications. Whole Slide Imaging: Current Applications and Future Directions (2022), 57–79.Google Scholar
- Pargorn Puttapirat, Haichuan Zhang, Yuchen Lian, Chunbao Wang, Xiangrong Zhang, Lixia Yao, and Chen Li. 2018. OpenHI-An open source framework for annotating histopathological image. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 1076–1082.Google ScholarCross Ref
- Jorge S Reis-Filho and Jakob Nikolas Kather. 2023. Overcoming the challenges to implementation of artificial intelligence in pathology. JNCI: Journal of the National Cancer Institute (2023), djad048.Google Scholar
- Hyuna Sung, Jacques Ferlay, Rebecca L Siegel, Mathieu Laversanne, Isabelle Soerjomataram, Ahmedin Jemal, and Freddie Bray. 2021. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 71, 3 (2021), 209–249.Google Scholar
- Thaína A Azevedo Tosta, Paulo Rogério de Faria, Leandro Alves Neves, and Marcelo Zanchetta do Nascimento. 2019. Computational normalization of H&E-stained histological images: Progress, challenges and future potential. Artificial intelligence in medicine 95 (2019), 118–132.Google Scholar
- Jeroen Van der Laak, Geert Litjens, and Francesco Ciompi. 2021. Deep learning in histopathology: the path to the clinic. Nature medicine 27, 5 (2021), 775–784.Google Scholar
Index Terms
- WSI2ML – An Open-Source Whole Slide Image Annotation Software for Machine Learning Applications
Recommendations
Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network
AbstractThe uncontrollable growth of cells in the breast tissue causes breast cancer which is the second most common type of cancer affecting women in the United States. Normally, human epidermal growth factor receptor 2 (HER2) proteins are ...
Highlights- HER2 overexpression is a prognostic marker in invasive breast cancer.
- Provided ...
Deep Machine Learning Histopathological Image Analysis for Renal Cancer Detection
ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial IntelligenceRenal cancer is one of the top causes of cancer-related deaths among men globally. Early detection of renal cancer is crucial because it can significantly improve the probability of survival rate. However, assessing the histopathological renal tissues ...
Toward an Open-source Toolkit for Machine Learning Education
SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science EducationMachine Learning (ML) has become one of the highly participated courses at the undergraduate level in Computer Science. Open-source ML libraries make it easy for students to implement papers, share ideas, and conduct experiments on large scale datasets. ...
Comments