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17.05.2024 | Akute myeloische Leukämie | Schwerpunkt
Schwerpunkt „KI in der Hämatologie und Onkologie“
Subfächer Innere Medizin
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Literatur
1.
Zurück zum Zitat Arber DA et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140(11):1200-28
Arber DA et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140(11):1200-28
2.
Zurück zum Zitat Shaver AC et al. Nuances of Morphology in Myelodysplastic Diseases in the Age of Molecular Diagnostics. Curr Hematol Malig Rep. 2017;12(5):448-54
Shaver AC et al. Nuances of Morphology in Myelodysplastic Diseases in the Age of Molecular Diagnostics. Curr Hematol Malig Rep. 2017;12(5):448-54
3.
Zurück zum Zitat Font P et al. Inter-observer variance with the diagnosis of myelodysplastic syndromes (MDS) following the 2008 WHO classification. Ann Hematol. 2013;92(1):19-24
Font P et al. Inter-observer variance with the diagnosis of myelodysplastic syndromes (MDS) following the 2008 WHO classification. Ann Hematol. 2013;92(1):19-24
4.
Zurück zum Zitat Fuentes-Arderiu X et al. Measurement uncertainty in manual differential leukocyte counting. Clin Chem Lab Med. 2009;47(1):112-5
Fuentes-Arderiu X et al. Measurement uncertainty in manual differential leukocyte counting. Clin Chem Lab Med. 2009;47(1):112-5
5.
6.
Zurück zum Zitat Matek C et al. Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. Nat Mach Intell. 2019;1(11):538-44
Matek C et al. Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. Nat Mach Intell. 2019;1(11):538-44
7.
Zurück zum Zitat Peng K et al. Automated bone marrow cell classification through dual attention gates dense neural networks. J Cancer Res Clin Oncol. 2023;149(19):16971-81
Peng K et al. Automated bone marrow cell classification through dual attention gates dense neural networks. J Cancer Res Clin Oncol. 2023;149(19):16971-81
8.
Zurück zum Zitat Matek C et al. Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set. Blood. 2021;138(20):1917-27
Matek C et al. Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set. Blood. 2021;138(20):1917-27
9.
Zurück zum Zitat Kockwelp J et al. Deep learning predicts therapy-relevant genetics in acute myeloid leukemia from Pappenheim-stained bone marrow smears. Blood Adv. 2024;8(1):70-9
Kockwelp J et al. Deep learning predicts therapy-relevant genetics in acute myeloid leukemia from Pappenheim-stained bone marrow smears. Blood Adv. 2024;8(1):70-9
10.
Zurück zum Zitat Jennifer SS et al. Sickle cell disease classification using deep learning. Heliyon. 2023;9(11):e22203
Jennifer SS et al. Sickle cell disease classification using deep learning. Heliyon. 2023;9(11):e22203
11.
Zurück zum Zitat Samek W et al. (Hrsg). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Cham, Schweiz: Springer Nature; 2019
Samek W et al. (Hrsg). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Cham, Schweiz: Springer Nature; 2019
12.
Zurück zum Zitat Ilse M et al. Attention-based Deep Multiple Instance Learning. In: Dy J, Krause A (Hrsg.). Proceedings of the 35th International Conference on Machine Learning. PMLR. Jul 10-15 2018; Stockholm, Schweden. 2018. S. 2127-36
Ilse M et al. Attention-based Deep Multiple Instance Learning. In: Dy J, Krause A (Hrsg.). Proceedings of the 35th International Conference on Machine Learning. PMLR. Jul 10-15 2018; Stockholm, Schweden. 2018. S. 2127-36
13.
Zurück zum Zitat Hehr M et al. Explainable AI identifies diagnostic cells of genetic AML subtypes. PLOS Digit Health. 2023;2(3):e0000187
Hehr M et al. Explainable AI identifies diagnostic cells of genetic AML subtypes. PLOS Digit Health. 2023;2(3):e0000187
14.
Zurück zum Zitat Eckardt JN et al. Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears. Leukemia. 2022;36(1):111-8
Eckardt JN et al. Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears. Leukemia. 2022;36(1):111-8
15.
Zurück zum Zitat Sadafi A et al. Attention Based Multiple Instance Learning for Classification of Blood Cell Disorders. In: Martel AL et al (Hrsg.). Medical Image Computing and Computer Assisted Intervention - MICCAI 2020. Okt 4-8 2020; Lima, Peru. Cham, Schweiz: Springer Nature. 2020. S. 246-56
Sadafi A et al. Attention Based Multiple Instance Learning for Classification of Blood Cell Disorders. In: Martel AL et al (Hrsg.). Medical Image Computing and Computer Assisted Intervention - MICCAI 2020. Okt 4-8 2020; Lima, Peru. Cham, Schweiz: Springer Nature. 2020. S. 246-56
16.
17.
18.
Zurück zum Zitat Acevedo A et al. A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data Brief. 2020;30:105474
Acevedo A et al. A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data Brief. 2020;30:105474
19.
Zurück zum Zitat Sadafi A et al. Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images. In: Frangi A et al. (Hrsg.). Information Processing in Medical Imaging. Jun 18-23 2023; San Carlos de Bariloche, Argentinien. Cham, Schweiz: Springer Nature. 2023. S. 170-82
Sadafi A et al. Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images. In: Frangi A et al. (Hrsg.). Information Processing in Medical Imaging. Jun 18-23 2023; San Carlos de Bariloche, Argentinien. Cham, Schweiz: Springer Nature. 2023. S. 170-82
Metadaten
- Titel
-
Schwerpunkt „KI in der Hämatologie und Onkologie“
Bildorientierte KI zur Unterstützung der zytomorphologischen Leukämiediagnostik - verfasst von
-
Dr. Dr. med. Christian Matek, M.Sc.
Prof. Dr. med. Karsten Spiekermann
Dr. Carsten Marr
- Publikationsdatum
- 17.05.2024
- Verlag
- Springer Medizin
- Schlagwörter
- Akute myeloische Leukämie
Künstliche Intelligenz - DOI
- https://doi.org/10.1007/s15004-024-0564-7
Bildnachweise
AI/© KKC Studio / stock.adobe.com