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Gabriele Campanella
Researcher at Memorial Sloan Kettering Cancer Center
Publications - 18
Citations - 1733
Gabriele Campanella is an academic researcher from Memorial Sloan Kettering Cancer Center. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 6, co-authored 14 publications receiving 813 citations. Previous affiliations of Gabriele Campanella include Cornell University.
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Journal ArticleDOI
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.
Gabriele Campanella,Gabriele Campanella,Matthew G. Hanna,Luke Geneslaw,Allen P. Miraflor,Vitor Werneck Krauss Silva,Klaus J. Busam,Edi Brogi,Victor E. Reuter,David S. Klimstra,Thomas J. Fuchs,Thomas J. Fuchs +11 more
TL;DR: A multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations, and has the ability to train accurate classification models at unprecedented scale.
Journal ArticleDOI
DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem
TL;DR: This study shows that an end‐to‐end encoder–decoder network can produce high quality PET images at a fraction of the time compared to conventional methods, and was successfully applied to real clinical data.
Posted Content
Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology.
TL;DR: A dataset consisting of 12,160 slides, two orders of magnitude larger than previous datasets in pathology and equivalent to 25 times the pixel count of the entire ImageNet dataset is gathered to train a deep learning model under the Multiple Instance Learning (MIL) assumption.
Journal ArticleDOI
Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.
Gabriele Campanella,Arjun Rajanna,Lorraine Corsale,Peter J. Schüffler,Yukako Yagi,Thomas J. Fuchs,Thomas J. Fuchs +6 more
TL;DR: A benchmark dataset for blur detection, an in-depth comparison of state-of-the art sharpness descriptors and their prediction performance within a random forest framework, and it is shown that convolution neural networks, like residual networks, can be used to train blur detectors from scratch.
Journal ArticleDOI
Structural and Thermodynamic Properties of Nanoparticle-Protein Complexes: A Combined SAXS and SANS Study.
Francesco Spinozzi,Giacomo Ceccone,Paolo Moretti,Gabriele Campanella,Claudio Ferrero,Sophie Combet,Isaac Ojea-Jiménez,Paolo Ghigna +7 more
TL;DR: It is demonstrated that the protein dissociation constant, the Hill coefficient, and the stoichiometry of the nanoparticle-protein complex are obtained with a high degree of confidence.