References

Method papers, data sources and neighbouring teaching material the series rests on

How to use this page

Every simulation and module page lists its own citations at the bottom. The list here covers every record in references.bib; when adding a simulation, add its sources to that file first.

Method papers, in reading order

  1. Attention-based MIL: Ilse, Tomczak and Welling (2018). Slide Bag Net’s pooling block comes from this paper.
  2. CLAM: Lu et al. (2021). Attention MIL applied to whole-slide images; clustering-constrained attention.
  3. TransMIL: Shao et al. (2021). Self-attention across patches; the basis of planned simulation 4.
  4. Clinical-grade weak supervision: Campanella et al. (2019). 44,000 slides; the proof that MIL works at scale.
  5. Mutation from H&E: Coudray et al. (2018, lung); Kather et al. (2019, MSI); Echle et al. (2020, clinical-grade MSI); Kather et al. (2020) and Fu et al. (2020), pan-cancer screens.
  6. Shortcut learning and site effects: Geirhos et al. (2020); Howard et al. (2021). The real counterpart of the lab trap.
  7. Stain normalisation and augmentation: Ruifrok and Johnston (2001, colour deconvolution); Macenko et al. (2009); Vahadane et al. (2016); Tellez et al. (2019, augmentation versus normalisation). The basis of Stain Shift.
  8. Foundation models: Chen et al. (2024, UNI); Xu et al. (2024, Prov-GigaPath). The real counterpart of the simulation’s “six measurements”.

Data sources

  • cBioPortal: cbioportal.org; API documentation at /api/swagger-ui. Cerami et al. (2012), Gao et al. (2013), de Bruijn et al. (2023).
  • TCGA LUAD: TCGA Research Network (2014); PanCancer Atlas (Hoadley et al., 2018); MC3 mutation calls (Ellrott et al., 2018).
  • GDC Data Portal: portal.gdc.cancer.gov: TCGA diagnostic slides (*-DX1.svs).

Neighbouring teaching material

  • Nucleus Net: the formal inspiration of this series; small neural networks on blood counts and nuclei with every node visible.
  • QuPath workshop for pathologists (Turkish): the measurement side; in particular the Evaluating AI tools, Stain normalisation and Pathology foundation models appendices.
  • EMPAIA Academy: a pathologist-oriented Colab workshop on machine-learning pitfalls.
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All records

Bruijn, Ino de, Ritika Kundra, Brooke Mastrogiacomo, Thinh Ngoc Tran, Luke Sikina, Tali Mazor, Xiang Li, et al. 2023. “Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal.” Cancer Research 83 (23): 3861–67. https://doi.org/10.1158/0008-5472.CAN-23-0816.
Campanella, Gabriele, Matthew G. Hanna, Luke Geneslaw, Allen Miraflor, Vitor Werneck Krauss Silva, Klaus J. Busam, Edi Brogi, Victor E. Reuter, David S. Klimstra, and Thomas J. Fuchs. 2019. “Clinical-Grade Computational Pathology Using Weakly Supervised Deep Learning on Whole Slide Images.” Nature Medicine 25: 1301–9. https://doi.org/10.1038/s41591-019-0508-1.
Cerami, Ethan, Jianjiong Gao, Ugur Dogrusoz, Benjamin E. Gross, Selcuk Onur Sumer, Bülent Arman Aksoy, Anders Jacobsen, et al. 2012. “The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data.” Cancer Discovery 2 (5): 401–4. https://doi.org/10.1158/2159-8290.CD-12-0095.
Chen, Richard J., Tong Ding, Ming Y. Lu, Drew F. K. Williamson, Guillaume Jaume, Andrew H. Song, Bowen Chen, et al. 2024. “Towards a General-Purpose Foundation Model for Computational Pathology.” Nature Medicine 30: 850–62. https://doi.org/10.1038/s41591-024-02857-3.
Coudray, Nicolas, Paolo Santiago Ocampo, Theodore Sakellaropoulos, Navneet Narula, Matija Snuderl, David Fenyö, Andre L. Moreira, Narges Razavian, and Aristotelis Tsirigos. 2018. “Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images Using Deep Learning.” Nature Medicine 24: 1559–67. https://doi.org/10.1038/s41591-018-0177-5.
Echle, Amelie, Heike Irmgard Grabsch, Philip Quirke, Piet A. van den Brandt, Nicholas P. West, Gordon G. A. Hutchins, Lara R. Heij, et al. 2020. “Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning.” Gastroenterology 159 (4): 1406–16. https://doi.org/10.1053/j.gastro.2020.06.021.
Ellrott, Kyle, Matthew H. Bailey, Gordon Saksena, Kyle R. Covington, Cyriac Kandoth, Chip Stewart, Julian Hess, et al. 2018. “Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines.” Cell Systems 6 (3): 271–281.e7. https://doi.org/10.1016/j.cels.2018.03.002.
Fu, Yu, Alexander W. Jung, Ramon Viñas Torne, Santiago Gonzalez, Harald Vöhringer, Artem Shmatko, Lucy R. Yates, Mercedes Jimenez-Linan, Luiza Moore, and Moritz Gerstung. 2020. “Pan-Cancer Computational Histopathology Reveals Mutations, Tumor Composition and Prognosis.” Nature Cancer 1: 800–810. https://doi.org/10.1038/s43018-020-0085-8.
Gao, Jianjiong, Bülent Arman Aksoy, Ugur Dogrusoz, Gideon Dresdner, Benjamin Gross, S. Onur Sumer, Yichao Sun, et al. 2013. “Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal.” Science Signaling 6 (269): pl1. https://doi.org/10.1126/scisignal.2004088.
Geirhos, Robert, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann. 2020. “Shortcut Learning in Deep Neural Networks.” Nature Machine Intelligence 2: 665–73. https://doi.org/10.1038/s42256-020-00257-z.
Hoadley, Katherine A., Christina Yau, Toshinori Hinoue, Denise M. Wolf, Alexander J. Lazar, Esther Drill, Ronglai Shen, et al. 2018. “Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.” Cell 173 (2): 291–304.e6. https://doi.org/10.1016/j.cell.2018.03.022.
Howard, Frederick M., James Dolezal, Sara Kochanny, Jefree Schulte, Heather Chen, Lara Heij, Dezheng Huo, et al. 2021. “The Impact of Site-Specific Digital Histology Signatures on Deep Learning Model Accuracy and Bias.” Nature Communications 12: 4423. https://doi.org/10.1038/s41467-021-24698-1.
Ilse, Maximilian, Jakub M. Tomczak, and Max Welling. 2018. “Attention-Based Deep Multiple Instance Learning.” In Proceedings of the 35th International Conference on Machine Learning (ICML), 80:2127–36. PMLR. https://proceedings.mlr.press/v80/ilse18a.html.
Kather, Jakob Nikolas, Lara R. Heij, Heike I. Grabsch, Chiara Loeffler, Amelie Echle, Hannah Sophie Muti, Jeremias Krause, et al. 2020. “Pan-Cancer Image-Based Detection of Clinically Actionable Genetic Alterations.” Nature Cancer 1: 789–99. https://doi.org/10.1038/s43018-020-0087-6.
Kather, Jakob Nikolas, Alexander T. Pearson, Niels Halama, Dirk Jäger, Jeremias Krause, Sven H. Loosen, Alexander Marx, et al. 2019. “Deep Learning Can Predict Microsatellite Instability Directly from Histology in Gastrointestinal Cancer.” Nature Medicine 25: 1054–56. https://doi.org/10.1038/s41591-019-0462-y.
Lu, Ming Y., Drew F. K. Williamson, Tiffany Y. Chen, Richard J. Chen, Matteo Barbieri, and Faisal Mahmood. 2021. “Data-Efficient and Weakly Supervised Computational Pathology on Whole-Slide Images.” Nature Biomedical Engineering 5: 555–70. https://doi.org/10.1038/s41551-020-00682-w.
Macenko, Marc, Marc Niethammer, J. 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, 1107–10. https://doi.org/10.1109/ISBI.2009.5193250.
Ruifrok, Arnout C., and Dennis A. Johnston. 2001. “Quantification of Histochemical Staining by Color Deconvolution.” Analytical and Quantitative Cytology and Histology 23 (4): 291–99.
Shao, Zhuchen, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, and Yongbing Zhang. 2021. “TransMIL: Transformer Based Correlated Multiple Instance Learning for Whole Slide Image Classification.” In Advances in Neural Information Processing Systems (NeurIPS). Vol. 34. https://arxiv.org/abs/2106.00908.
Tellez, David, Geert Litjens, Péter Bándi, Wouter Bulten, John-Melle Bokhorst, Francesco Ciompi, and Jeroen van der Laak. 2019. “Quantifying the Effects of Data Augmentation and Stain Color Normalization in Convolutional Neural Networks for Computational Pathology.” Medical Image Analysis 58: 101544. https://doi.org/10.1016/j.media.2019.101544.
The Cancer Genome Atlas Research Network. 2014. “Comprehensive Molecular Profiling of Lung Adenocarcinoma.” Nature 511: 543–50. https://doi.org/10.1038/nature13385.
Vahadane, Abhishek, Tingying Peng, Amit Sethi, Shadi Albarqouni, Lichao Wang, Maximilian Baust, Katja Steiger, Anna Melissa Schlitter, Irene Esposito, and Nassir Navab. 2016. “Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images.” IEEE Transactions on Medical Imaging 35 (8): 1962–71. https://doi.org/10.1109/TMI.2016.2529665.
Xu, Hanwen, Naoto Usuyama, Jaspreet Bagga, Sheng Zhang, Rajesh Rao, Tristan Naumann, Cliff Wong, et al. 2024. “A Whole-Slide Foundation Model for Digital Pathology from Real-World Data.” Nature 630: 181–90. https://doi.org/10.1038/s41586-024-07441-w.