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Your search keyword '"Wapinski, Ilan"' showing total 187 results

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187 results on '"Wapinski, Ilan"'

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1. AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases

2. AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis

3. Artificial Intelligence–Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study

4. A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.

6. Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab

7. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

8. Artificial intelligence–powered assessment of pathologic response to neoadjuvant atezolizumab in patients with non-small cell lung cancer: results from the LCMC3 study

9. Proto-genes and de novo gene birth

11. P1246: CELL TYPE IDENTIFICATION USING MULTIPLEX IMMUNOFLUORESCENCE (MIF) GUIDED MACHINE LEARNING IN DLBCL

12. Deep-learning quantified cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer types

13. AI-based histologic scoring enables automated and reproducible assessment of enrollment criteria and endpoints in NASH clinical trials

14. Abstract 5422: Machine learning models identify key histological features of renal cell carcinoma subtypes

15. Abstract 5705: Digital pathology based prognostic & predictive biomarkers in metastatic non-small cell lung cancer

16. Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH

17. Abstract P6-04-08: Machine learning-based characterization of the breast cancer tumor microenvironment for assessment of neoadjuvant-treatment response

18. Abstract P4-09-08: AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer

19. Abstract P5-02-09: Quantitative analysis of fiber-level collagen features in H&E whole-slide images predicts neoadjuvant therapy response in patients with HER2+ breast cancer

21. MACHINE LEARNING-BASED PREDICTION OF GEBOES SCORE AND HISTOLOGIC IMPROVEMENT AND REMISSION THRESHOLDS IN ULCERATIVE COLITIS

22. QUANTITATIVE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE (AI)-POWERED APPROACHES TO PREDICT ULCERATIVE COLITIS DISEASE ACTIVITY FROM HEMATOXYLIN AND EOSIN (H&E)-STAINED WHOLE SLIDE IMAGES (WSI)

23. 1282 Concordance analysis of AI-powered CD8 quantification and automated CD8 topology with manual histopathological assessment across seven solid tumor types

24. Machine learning-enabled continuous scoring of histologic features facilitates prediction of clinical disease progression in patients with non-alcoholic steatohepatitis

25. Abstract 471: AIM PD-L1-NSCLC: Artificial intelligence-powered PD-L1 quantification for accurate prediction of tumor proportion score in diverse, multi-stain clinical tissue samples

26. Abstract 1922: Application of an interpretable graph neural network to predict gene expression signatures associated with tertiary lymphoid structures in histopathological images

27. Abstract CT112: AI-powered and manual assessment of PD-L1 are comparable in predicting response to neoadjuvant atezolizumab in patients (pts) with resectable non-squamous, non-small cell lung cancer (NSCLC)

29. Abstract 464: AI-powered segmentation and analysis of nuclei morphology predicts genomic and clinical markers in multiple cancer types

30. Comparative Functional Genomics of the Fission Yeasts

32. Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology

33. A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis

35. Overdispersion of the molecular clock varies between yeast, Drosophila and mammals

36. Natural history and evolutionary principles of gene duplication in fungi

37. Machine learning models to quantify HER2 for real-time tissue image analysis in prospective clinical trials.

39. Mo1754 MACHINE LEARNING-BASED PREDICTION OF GEBOES SCORE AND HISTOLOGIC IMPROVEMENT AND REMISSION THRESHOLDS IN ULCERATIVE COLITIS

42. Abstract PD6-04: Deep-learning based prediction of homologous recombination deficiency (hrd) status from histological features in breast cancer; a research study

43. Abstract 2017: Association of digital and manual quantification of tumor PD-L1 expression with outcomes in nivolumab-treated patients

44. Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer

45. Machine learning identifies histologic features associated with regression of cirrhosis in treatment for chronic hepatitis B

46. Machine learning models accurately interpret liver histology and are associated with disease progression in patients with primary sclerosing cholangitis

47. Machine learning models identify novel histologic features predictive of clinical disease progression in patients with advanced fibrosis due to non-alcoholic steatohepatitis

48. Machine learning-based identification of predictive features of the tumor micro-environment and vasculature in NSCLC patients using the IMpower150 study.

49. Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium

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