Co-Authors
This is a "connection" page, showing publications co-authored by Frederick Howard and James Dolezal.
Connection Strength
1.219
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Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features. Sci Adv. 2024 Nov 15; 10(46):eadq0856.
Score: 0.241
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Slideflow: deep learning for digital histopathology with real-time whole-slide visualization. BMC Bioinformatics. 2024 Mar 27; 25(1):134.
Score: 0.231
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Deep learning generates synthetic cancer histology for explainability and education. NPJ Precis Oncol. 2023 May 29; 7(1):49.
Score: 0.218
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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.
Score: 0.216
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The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat Commun. 2021 07 20; 12(1):4423.
Score: 0.192
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Analysis of AI foundation model features decodes the histopathologic landscape of HPV-positive head and neck squamous cell carcinomas. Oral Oncol. 2025 Apr; 163:107207.
Score: 0.062
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Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning. EBioMedicine. 2024 Sep; 107:105276.
Score: 0.059