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Connection

Co-Authors

This is a "connection" page, showing publications co-authored by Alexander Pearson and James Dolezal.
Connection Strength

4.419
  1. Slideflow: deep learning for digital histopathology with real-time whole-slide visualization. BMC Bioinformatics. 2024 Mar 27; 25(1):134.
    View in: PubMed
    Score: 0.984
  2. Deep learning generates synthetic cancer histology for explainability and education. NPJ Precis Oncol. 2023 May 29; 7(1):49.
    View in: PubMed
    Score: 0.929
  3. Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology. Nat Commun. 2022 11 02; 13(1):6572.
    View in: PubMed
    Score: 0.893
  4. Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features. Mod Pathol. 2021 05; 34(5):862-874.
    View in: PubMed
    Score: 0.783
  5. Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence. NPJ Breast Cancer. 2023 Apr 14; 9(1):25.
    View in: PubMed
    Score: 0.230
  6. Applications of Deep Learning in Endocrine Neoplasms. Surg Pathol Clin. 2023 Mar; 16(1):167-176.
    View in: PubMed
    Score: 0.225
  7. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat Commun. 2021 07 20; 12(1):4423.
    View in: PubMed
    Score: 0.204
  8. Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review. J Oral Pathol Med. 2023 Mar; 52(3):197-205.
    View in: PubMed
    Score: 0.057
  9. Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images. Front Med (Lausanne). 2023; 10:1058919.
    View in: PubMed
    Score: 0.057
  10. The EU-funded I3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy. Clin Lung Cancer. 2023 06; 24(4):381-387.
    View in: PubMed
    Score: 0.057
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.