420 results on '"Yuan, Yinyin"'
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2. Bridging clinic and wildlife care with AI-powered pan-species computational pathology
3. Longitudinal Assessment of Tumor-Infiltrating Lymphocytes in Primary Breast Cancer Following Neoadjuvant Radiation Therapy
4. Cell abundance aware deep learning for cell detection on highly imbalanced pathological data
5. Glioma Classification Using Multimodal Radiology and Histology Data
6. Self-supervised deep learning for highly efficient spatial immunophenotyping
7. ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images
8. Glioblastoma cell fate is differentially regulated by the microenvironments of the tumor bulk and infiltrative margin
9. DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence
10. Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns
11. DeepMIF: Deep Learning Based Cell Profiling for Multispectral Immunofluorescence Images with Graphical User Interface
12. Self-supervised Antigen Detection Artificial Intelligence (SANDI)
13. Capturing global spatial context for accurate cell classification in skin cancer histology
14. DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images
15. Deconvolving convolution neural network for cell detection
16. Spatial interplay of tissue hypoxia and T-cell regulation in ductal carcinoma in situ
17. Glioma Classification Using Multimodal Radiology and Histology Data
18. Classifying the evolutionary and ecological features of neoplasms
19. DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence
20. Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns
21. Self-supervised Antigen Detection Artificial Intelligence (SANDI)
22. Supplementary Table 10 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
23. Table 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
24. Figure 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
25. Figure 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
26. Figure 3 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
27. Figure 5 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
28. Figure 4 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
29. Table 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
30. Supplementary Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
31. Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
32. Figure 6 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
33. Dysregulated SASS6 expression promotes increased ciliogenesis and cell invasion phenotypes
34. Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer
35. Deciphering the diversity and sequence of extracellular matrix and cellular spatial patterns in lung adenocarcinoma using topological data analysis
36. Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology
37. Biomarkers for site-specific response to neoadjuvant chemotherapy in epithelial ovarian cancer: relating MRI changes to tumour cell load and necrosis
38. Artificial intelligence-based morphometric signature to identify ductal carcinoma in situ with low risk of progression to invasive breast cancer
39. Deep learning enables spatial mapping of the mosaic microenvironment of myeloma bone marrow trephine biopsies
40. Unmasking the immune microecology of ductal carcinoma in situ with deep learning
41. Glioma Classification Using Multimodal Radiology and Histology Data
42. The T cell differentiation landscape is shaped by tumour mutations in lung cancer
43. A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes
44. Spatial analyses of immune cell infiltration in cancer: current methods and future directions. A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
45. Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group
46. The tumour ecology of quiescence: Niches across scales of complexity
47. Statistical inference from large-scale genomic data
48. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
49. Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer
50. Figure 5 from Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma
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