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Gabriele Campanella

Showing results (1-10 of 12) with videos related to

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Head and Neck Pathology|January 8, 2026
An Introduction to Pathology Foundation ModelsBrandon Veremis, Shengjia Chen, Gabriele Campanella
Medical Image Analysis|April 8, 2019
DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problemIda Häggström, C Ross Schmidtlein, Gabriele Campanella, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|December 16, 2017
Towards machine learned quality control: A benchmark for sharpness quantification in digital pathologyGabriele Campanella, Arjun R Rajanna, Lorraine Corsale, et al.
Langmuir : the ACS Journal of Surfaces and Colloids|February 8, 2017
Structural and Thermodynamic Properties of Nanoparticle-Protein Complexes: A Combined SAXS and SANS StudyFrancesco Spinozzi, Giacomo Ceccone, Paolo Moretti, et al.
Journal of the American Academy of Dermatology|September 14, 2020
A deep learning algorithm with high sensitivity for the detection of basal cell carcinoma in Mohs micrographic surgery frozen sectionsGabriele Campanella, Kishwer S Nehal, Erica H Lee, et al.
Nature Medicine|July 17, 2019
Clinical-grade computational pathology using weakly supervised deep learning on whole slide imagesGabriele Campanella, Matthew G Hanna, Luke Geneslaw, et al.
Arxiv|March 30, 2026
GOLDMARK: Governed Outcome-Linked Diagnostic Model Assessment Reference KitChad Vanderbilt, Gabriele Campanella, Siddharth Singi, et al.
The Journal of Investigative Dermatology|July 15, 2021
Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal MicroscopyGabriele Campanella, Cristian Navarrete-Dechent, Konstantinos Liopyris, et al.
Nature Communications|April 16, 2025
A clinical benchmark of public self-supervised pathology foundation modelsGabriele Campanella, Shengjia Chen, Manbir Singh, et al.
The Lancet. Digital Health|December 22, 2023
Deep learning for [<sup>18</sup>F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysisIda Häggström, Doris Leithner, Jennifer Alvén, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Head and Neck Pathology|January 8, 2026
An Introduction to Pathology Foundation ModelsBrandon Veremis, Shengjia Chen, Gabriele Campanella
Medical Image Analysis|April 8, 2019
DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problemIda Häggström, C Ross Schmidtlein, Gabriele Campanella, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|December 16, 2017
Towards machine learned quality control: A benchmark for sharpness quantification in digital pathologyGabriele Campanella, Arjun R Rajanna, Lorraine Corsale, et al.
Langmuir : the ACS Journal of Surfaces and Colloids|February 8, 2017
Structural and Thermodynamic Properties of Nanoparticle-Protein Complexes: A Combined SAXS and SANS StudyFrancesco Spinozzi, Giacomo Ceccone, Paolo Moretti, et al.
Journal of the American Academy of Dermatology|September 14, 2020
A deep learning algorithm with high sensitivity for the detection of basal cell carcinoma in Mohs micrographic surgery frozen sectionsGabriele Campanella, Kishwer S Nehal, Erica H Lee, et al.
Nature Medicine|July 17, 2019
Clinical-grade computational pathology using weakly supervised deep learning on whole slide imagesGabriele Campanella, Matthew G Hanna, Luke Geneslaw, et al.
Arxiv|March 30, 2026
GOLDMARK: Governed Outcome-Linked Diagnostic Model Assessment Reference KitChad Vanderbilt, Gabriele Campanella, Siddharth Singi, et al.
The Journal of Investigative Dermatology|July 15, 2021
Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal MicroscopyGabriele Campanella, Cristian Navarrete-Dechent, Konstantinos Liopyris, et al.
Nature Communications|April 16, 2025
A clinical benchmark of public self-supervised pathology foundation modelsGabriele Campanella, Shengjia Chen, Manbir Singh, et al.
The Lancet. Digital Health|December 22, 2023
Deep learning for [<sup>18</sup>F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysisIda Häggström, Doris Leithner, Jennifer Alvén, et al.
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