Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23436, first published .
Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study

Journals

  1. Maron R, Haggenmüller S, von Kalle C, Utikal J, Meier F, Gellrich F, Hauschild A, French L, Schlaak M, Ghoreschi K, Kutzner H, Heppt M, Haferkamp S, Sondermann W, Schadendorf D, Schilling B, Hekler A, Krieghoff-Henning E, Kather J, Fröhling S, Lipka D, Brinker T. Robustness of convolutional neural networks in recognition of pigmented skin lesions. European Journal of Cancer 2021;145:81 View
  2. Schömig-Markiefka B, Pryalukhin A, Hulla W, Bychkov A, Fukuoka J, Madabhushi A, Achter V, Nieroda L, Büttner R, Quaas A, Tolkach Y. Quality control stress test for deep learning-based diagnostic model in digital pathology. Modern Pathology 2021;34(12):2098 View
  3. Sauter D, Lodde G, Nensa F, Schadendorf D, Livingstone E, Kukuk M. Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology. Sensors 2022;22(14):5346 View
  4. Bülow R, Hölscher D, Boor P. Automatische Bildanalyse und künstliche Intelligenz in der Nephropathologie. Die Nephrologie 2022;17(6):369 View
  5. Kiehl L, Kuntz S, Höhn J, Jutzi T, Krieghoff-Henning E, Kather J, Holland-Letz T, Kopp-Schneider A, Chang-Claude J, Brobeil A, von Kalle C, Fröhling S, Alwers E, Brenner H, Hoffmeister M, Brinker T. Deep learning can predict lymph node status directly from histology in colorectal cancer. European Journal of Cancer 2021;157:464 View
  6. Couture H. Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review. Journal of Personalized Medicine 2022;12(12):2022 View
  7. Mosquera-Zamudio A, Launet L, Tabatabaei Z, Parra-Medina R, Colomer A, Oliver Moll J, Monteagudo C, Janssen E, Naranjo V. Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review. Cancers 2022;15(1):42 View
  8. Bankhead P. Developing image analysis methods for digital pathology. The Journal of Pathology 2022;257(4):391 View
  9. Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Modern Pathology 2022;35(1):23 View
  10. Scalco R, Hamsafar Y, White C, Schneider J, Reichard R, Prokop S, Perrin R, Nelson P, Mooney S, Lieberman A, Kukull W, Kofler J, Keene C, Kapasi A, Irwin D, Gutman D, Flanagan M, Crary J, Chan K, Murray M, Dugger B. The status of digital pathology and associated infrastructure within Alzheimer’s Disease Centers. Journal of Neuropathology & Experimental Neurology 2023;82(3):202 View
  11. Orsulic S, John J, Walts A, Gertych A. Computational pathology in ovarian cancer. Frontiers in Oncology 2022;12 View
  12. Homeyer A, Geißler C, Schwen L, Zakrzewski F, Evans T, Strohmenger K, Westphal M, Bülow R, Kargl M, Karjauv A, Munné-Bertran I, Retzlaff C, Romero-López A, Sołtysiński T, Plass M, Carvalho R, Steinbach P, Lan Y, Bouteldja N, Haber D, Rojas-Carulla M, Vafaei Sadr A, Kraft M, Krüger D, Fick R, Lang T, Boor P, Müller H, Hufnagl P, Zerbe N. Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology. Modern Pathology 2022;35(12):1759 View
  13. Echle A, Laleh N, Schrammen P, West N, Trautwein C, Brinker T, Gruber S, Buelow R, Boor P, Grabsch H, Quirke P, Kather J. Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review. ImmunoInformatics 2021;3-4:100008 View
  14. Dudin O, Mintser O, Sulaieva O. ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE. Proceedings of the Shevchenko Scientific Society. Medical Sciences 2021;65(2) View
  15. Schneider L, Laiouar-Pedari S, Kuntz S, Krieghoff-Henning E, Hekler A, Kather J, Gaiser T, Fröhling S, Brinker T. Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review. European Journal of Cancer 2022;160:80 View
  16. Subramanian H, Subramanian S. Improving Diagnosis Through Digital Pathology: Proof-of-Concept Implementation Using Smart Contracts and Decentralized File Storage. Journal of Medical Internet Research 2022;24(3):e34207 View
  17. Jiang J, Tekin B, Yuan L, Armasu S, Winham S, Goode E, Liu H, Huang Y, Guo R, Wang C. Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma. Frontiers in Medicine 2022;9 View
  18. Plass M, Kargl M, Kiehl T, Regitnig P, Geißler C, Evans T, Zerbe N, Carvalho R, Holzinger A, Müller H. Explainability and causability in digital pathology. The Journal of Pathology: Clinical Research 2023;9(4):251 View
  19. Han C, Pan Y, Liu C, Yang X, Li J, Wang K, Sun Z, Liu H, Jin G, Fang F, Pan X, Tang T, Chen X, Pang S, Ma L, Wang X, Ren Y, Liu M, Liu F, Jiang M, Zhao J, Lu C, Lu Z, Gao D, Jiang Z, Pei J. Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment. Frontiers in Oncology 2023;13 View
  20. Farris A, Alexander M, Balis U, Barisoni L, Boor P, Bülow R, Cornell L, Demetris A, Farkash E, Hermsen M, Hogan J, Kain R, Kers J, Kong J, Levenson R, Loupy A, Naesens M, Sarder P, Tomaszewski J, van der Laak J, van Midden D, Yagi Y, Solez K. Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments. Transplant International 2023;36 View
  21. Evans H, Snead D. Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them?. Histopathology 2024;84(2):279 View
  22. Scalco R, Saito N, Beckett L, Nguyen M, Huie E, Wang H, Flaherty D, Honig L, DeCarli C, Rissman R, Teich A, Jin L, Dugger B. The neuropathological landscape of Hispanic and non-Hispanic White decedents with Alzheimer disease. Acta Neuropathologica Communications 2023;11(1) View
  23. Giarnieri E, Scardapane S. Towards Artificial Intelligence Applications in Next Generation Cytopathology. Biomedicines 2023;11(8):2225 View
  24. Gonzalez R, Nejat P, Saha A, Campbell C, Norgan A, Lokker C. Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review. Journal of Pathology Informatics 2024;15:100348 View
  25. Sauter D, Lodde G, Nensa F, Schadendorf D, Livingstone E, Kukuk M. Deep learning in computational dermatopathology of melanoma: A technical systematic literature review. Computers in Biology and Medicine 2023;163:107083 View
  26. Zhao Y, Wang X, Sun T, Shan P, Zhan Z, Zhao Z, Jiang Y, Qu M, Lv Q, Wang Y, Liu P, Chen S. Artificial intelligence-driven electrochemical immunosensing biochips in multi-component detection. Biomicrofluidics 2023;17(4) View
  27. Yee J, Rosendahl C, Aoude L. The role of artificial intelligence and convolutional neural networks in the management of melanoma: a clinical, pathological, and radiological perspective. Melanoma Research 2024;34(2):96 View
  28. Selvaraj J, Jayanthy A. AUTOMATIC POLYP SEMANTIC SEGMENTATION USING WIRELESS CAPSULE ENDOSCOPY IMAGES WITH VARIOUS CONVOLUTIONAL NEURAL NETWORK AND OPTIMIZATION TECHNIQUES: A COMPARISON AND PERFORMANCE EVALUATION. Biomedical Engineering: Applications, Basis and Communications 2023;35(06) View
  29. Weng W, Sellergen A, Kiraly A, D’Amour A, Park J, Pilgrim R, Pfohl S, Lau C, Natarajan V, Azizi S, Karthikesalingam A, Cole-Lewis H, Matias Y, Corrado G, Webster D, Shetty S, Prabhakara S, Eswaran K, Celi L, Liu Y. An intentional approach to managing bias in general purpose embedding models. The Lancet Digital Health 2024;6(2):e126 View
  30. Llamas-Velasco M, Ovejero-Merino E. Inteligencia artificial en el diagnóstico dermatopatológico. Piel 2024;39(8):512 View
  31. Malik F, Yousaf M, Sial H, Viriri S. Exploring dermoscopic structures for melanoma lesions' classification. Frontiers in Big Data 2024;7 View
  32. Ong Ly C, Unnikrishnan B, Tadic T, Patel T, Duhamel J, Kandel S, Moayedi Y, Brudno M, Hope A, Ross H, McIntosh C. Shortcut learning in medical AI hinders generalization: method for estimating AI model generalization without external data. npj Digital Medicine 2024;7(1) View
  33. Goldstein Y, Cohen O, Wald O, Bavli D, Kaplan T, Benny O. Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning. Science Advances 2024;10(22) View
  34. Humphries M, Kaye D, Stankeviciute G, Halliwell J, Wright A, Bansal D, Brettle D, Treanor D. Development of a multi‐scanner facility for data acquisition for digital pathology artificial intelligence. The Journal of Pathology 2024;264(1):80 View
  35. Murchan P, Ó Broin P, Baird A, Sheils O, P Finn S. Deep feature batch correction using ComBat for machine learning applications in computational pathology. Journal of Pathology Informatics 2024;15:100396 View
  36. Matthews G, McGenity C, Bansal D, Treanor D. Public evidence on AI products for digital pathology. npj Digital Medicine 2024;7(1) View