Kaynaklar

Serinin dayandığı yöntem makaleleri, veri kaynakları ve komşu öğretim malzemeleri

Nasıl kullanılır

Her simülasyon ve modül sayfası kendi atıflarını sayfa sonunda listeler. Buradaki liste, references.bib dosyasındaki tüm kayıtları kapsar; yeni bir simülasyon eklerken kaynağını önce o dosyaya ekleyin.

Yöntem makaleleri — okuma sırası

  1. Dikkat tabanlı MIL — Ilse, Tomczak ve Welling (2018). Slayt Torba Ağı’nın havuzlama bloğu bu makaledendir.
  2. CLAM — Lu ve ark. (2021). Dikkat MIL’in tüm slayt görüntüsüne uygulanması; kümelemeyle kısıtlanmış dikkat.
  3. TransMIL — Shao ve ark. (2021). Karolar arası öz-dikkat; planlı 4. simülasyonun temeli.
  4. Klinik düzeyde zayıf denetim — Campanella ve ark. (2019). 44.000 slayt; MIL’in ölçekte çalıştığının kanıtı.
  5. H&E’den mutasyon — Coudray ve ark. (2018, akciğer); Kather ve ark. (2019, MSI); Echle ve ark. (2020, klinik düzey MSI); Kather ve ark. (2020) ve Fu ve ark. (2020) pan-kanser taramalar.
  6. Kestirme öğrenme ve merkez etkisi — Geirhos ve ark. (2020); Howard ve ark. (2021). Laboratuvar tuzağının gerçek karşılığı.
  7. Boya normalizasyonu ve artırma — Ruifrok ve Johnston (2001, renk dekonvolüsyonu); Macenko ve ark. (2009); Vahadane ve ark. (2016); Tellez ve ark. (2019, artırma ile normalizasyonun karşılaştırması). Boya Kayması’nın temeli.
  8. Temel modeller — Chen ve ark. (2024, UNI); Xu ve ark. (2024, Prov-GigaPath). Simülasyondaki “altı ölçüm”ün gerçek karşılığı.

Veri kaynakları

  • cBioPortal — cbioportal.org; API belgesi /api/swagger-ui. Cerami ve ark. (2012), Gao ve ark. (2013), de Bruijn ve ark. (2023).
  • TCGA LUAD — TCGA Araştırma Ağı (2014); PanCancer Atlas (Hoadley ve ark., 2018); MC3 mutasyon çağrıları (Ellrott ve ark., 2018).
  • GDC Data Portal — portal.gdc.cancer.gov: TCGA tanısal slaytları (*-DX1.svs).

Komşu öğretim malzemeleri

  • Nucleus Net — bu serinin biçimsel esini: tam kan sayımı ve çekirdek üzerinde, her düğümü görünür küçük sinir ağları.
  • Patologlar için QuPath atölyesi — ölçüm tarafı; özellikle Yapay zekâ araçlarını değerlendirme, Boya normalizasyonu ve Patoloji temel modelleri ekleri.
  • EMPAIA Academy — makine öğrenmesi tuzakları üzerine patolog odaklı Colab atölyesi.
Başa dön

Tüm kayıtlar

Bruijn, Ino de, Ritika Kundra, Brooke Mastrogiacomo, Thinh Ngoc Tran, Luke Sikina, Tali Mazor, Xiang Li, vd. 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, ve 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, vd. 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, vd. 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, ve 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, vd. 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, vd. 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, ve 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, vd. 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, ve 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, vd. 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, vd. 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, ve Max Welling. 2018. “Attention-based Deep Multiple Instance Learning”. Içinde 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, vd. 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, vd. 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, ve 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, ve Nancy E. Thomas. 2009. “A method for normalizing histology slides for quantitative analysis”. Içinde 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1107-10. https://doi.org/10.1109/ISBI.2009.5193250.
Ruifrok, Arnout C., ve 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, ve Yongbing Zhang. 2021. “TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification”. Içinde Advances in Neural Information Processing Systems (NeurIPS). C. 34. https://arxiv.org/abs/2106.00908.
Tellez, David, Geert Litjens, Péter Bándi, Wouter Bulten, John-Melle Bokhorst, Francesco Ciompi, ve 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, ve 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, vd. 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.