31 results on '"Pohlkamp, Christian"'
Search Results
2. Genomic landscape of CCUS compared to MDS and its implications on risk prediction
3. Risk assessment according to IPSS-M is superior to AML ELN risk classification in MDS/AML overlap patients defined by ICC
4. AML classification in the year 2023: How to avoid a Babylonian confusion of languages
5. The clinical and genomic landscape of patients with DDX41 variants identified during diagnostic sequencing
6. Artificial intelligence in hematological diagnostics: Game changer or gadget?
7. Germline variants drive myelodysplastic syndrome in young adults
8. Improved survival in metastatic breast cancer: results from a 20-year study involving 1033 women treated at a single comprehensive cancer center
9. P694: RISK ASSESSMENT ACCORDING TO IPSS-M IS SUPERIOR TO AML ELN 2022 RISK CLASSIFICATION IN MDS/AML PATIENTS DEFINED BY ICC – IS AN INCLUSION INTO AML TRIALS JUSTIFIED?
10. P533: A FULLY AUTOMATED SUPERVISED AI-BASED CELL CLASSIFIER TO ACCURATELY FLAG PATHOLOGICAL SIGNATURE CELLS IN 28,285 REAL-WORLD BLOOD SMEARS
11. Molecular patterns in cytopenia patients with or without evidence of myeloid neoplasm—a comparison of 756 cases
12. Explainable AI identifies diagnostic cells of genetic AML subtypes
13. AML and MDS Classification According to Who 2022 and International Consensus Classification: Do We Invent a Babylonian Confusion of Languages?
14. Machine Learning Algorithm Correctly Identifies 95% of Cells in Differential Count of Blood Smears: A Prospective Study on >29,000 Cases and >17 Million Single Cells
15. A Fully Automated Digital Workflow for Assessment of Bone Marrow Cytomorphology Based on Single Cell Detection and Classification with AI
16. Whole Genome Sequencing Identifies Non-KIT Mutations and Cytogenetic Aberrations in Systemic Mastocytosis but Has Limited Sensitivity for Detection of KIT D816V
17. A Completely Digital Workflow for Differentials in Bone Marrow Cytomorphology Supported By Machine Learning Provides Promising Results in Object Detection
18. Automated Peripheral Blood Cell Differentiation Using Artificial Intelligence - a Study with More Than 10,000 Routine Samples in a Specialized Leukemia Laboratory
19. Modification of the ELN Classification 2022 Refines Risk Assessment in MDS/AML Patients
20. Proposal ofmyeloid Neoplasms with MYC-Positive Double Minutes As a Distinct Entity
21. Automated Cytomorphological Analysis of Bone Marrow Samples: A Proof-of-Principle Study for AI-Based Classification on a Real-Life Data Set of 979 Unselected Cases
22. Explainable End-to-End Supervised Learning Identifies Myelodysplastic Neoplasms in Bone Marrow Smears
23. Genomic Landscape of Ccus Compared to MDS Indicates a Potential Applicability of the IPSS-M
24. Putative Germline Variants in the Predisposition Genes DDX41, ETV6 and GATA2 investigated in 1,228 Patients with Sporadic AML or MDS
25. Machine Learning (ML) Can Successfully Support Microscopic Differential Counts of Peripheral Blood Smears in a High Throughput Hematology Laboratory
26. Machine Learning Algorithm Correctly Identifies 95% of Cells in Differential Count of Blood Smears: A Prospective Study on >29,000 Cases and >17 Million Single Cells
27. Evidence of clonality in cases of hypereosinophilia of undetermined significance
28. Überlebenszeiten von Patientinnen mit metastasierten Mammakarzinom, die in der Inneren Klinik (Tumorforschung) des Universitätsklinikums Essen behandelt wurden : Erstvorstellungszeitraum 1995 - 1999
29. The clinical and genomic landscape of patients with DDX41variants identified during diagnostic sequencing
30. Putative Germline Variants in the Predisposition Genes DDX41, ETV6and GATA2investigated in 1,228 Patients with Sporadic AML or MDS
31. Whole Genome Sequencing Identifies Non-KITMutations and Cytogenetic Aberrations in Systemic Mastocytosis but Has Limited Sensitivity for Detection of KITD816V
Catalog
Books, media, physical & digital resources
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.