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ORCIDGabriele Campanella
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2020 – today
- 2024
[i9]Gabriele Campanella, Eugene Fluder, Jennifer Zeng, Chad M. Vanderbilt, Thomas J. Fuchs:
Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling. CoRR abs/2403.04865 (2024)- [i8]Gabriele Campanella, Shengjia Chen
, Ruchika Verma, Jennifer Zeng, Aryeh Stock, Matt Croken, Brandon Veremis, Abdulkadir Elmas, Kuan-Lin Huang, Ricky Kwan, Jane Houldsworth, Adam Schoenfeld, Chad M. Vanderbilt:
A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models. CoRR abs/2407.06508 (2024) - [i7]Shengjia Chen, Gabriele Campanella, Abdulkadir Elmas, Aryeh Stock, Jennifer Zeng, Alexandros D. Polydorides, Adam Schoenfeld, Kuan-Lin Huang, Jane Houldsworth, Chad M. Vanderbilt, Thomas J. Fuchs:
Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective. CoRR abs/2407.07841 (2024) - 2023
- [i6]Gabriele Campanella, Ricky Kwan, Eugene Fluder, Jennifer Zeng, Aryeh Stock, Brandon Veremis, Alexandros D. Polydorides, Cyrus Hedvat, Adam Schoenfeld, Chad M. Vanderbilt, Patricia H. Kovatch, Carlos Cordon-Cardo, Thomas J. Fuchs:
Computational Pathology at Health System Scale - Self-Supervised Foundation Models from Three Billion Images. CoRR abs/2310.07033 (2023) - 2022
- [i5]Gabriele Campanella, David Joon Ho, Ida Häggström, Anton S. Becker, Jason Chang, Chad M. Vanderbilt, Thomas J. Fuchs:
H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma. CoRR abs/2206.10573 (2022) - [i4]Gabriele Campanella, Lucas Kook, Ida Häggström, Torsten Hothorn, Thomas J. Fuchs:
Deep conditional transformation models for survival analysis. CoRR abs/2210.11366 (2022) - 2020
- [c2]Chensu Xie, Hassan Muhammad, Chad M. Vanderbilt, Raul Caso, Dig Vijay Kumar Yarlagadda, Gabriele Campanella, Thomas J. Fuchs:
Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning. MIDL 2020: 843-856
2010 – 2019
- 2019
- [j2]Ida Häggström, Charles Ross Schmidtlein, Gabriele Campanella, Thomas J. Fuchs:
DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem. Medical Image Anal. 54: 253-262 (2019) - [c1]Hassan Muhammad, Carlie S. Sigel, Gabriele Campanella, Thomas Börner, Linda M. Pak, Stefan Büttner, Jan N. M. IJzermans, Bas Groot Koerkamp, Michael Doukas, William R. Jarnagin, Amber L. Simpson, Thomas J. Fuchs:
Unsupervised Subtyping of Cholangiocarcinoma Using a Deep Clustering Convolutional Autoencoder. MICCAI (1) 2019: 604-612 - [i3]Hassan Muhammad, Carlie S. Sigel, Gabriele Campanella, Thomas Börner, Linda M. Pak, Stefan Büttner, Jan N. M. IJzermans, Bas Groot Koerkamp, Michael Doukas, William R. Jarnagin, Amber L. Simpson, Thomas J. Fuchs:
Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary. CoRR abs/1903.05257 (2019) - 2018
- [j1]Gabriele Campanella, Arjun R. Rajanna, Lorraine Corsale, Peter J. Schüffler, Yukako Yagi, Thomas J. Fuchs:
Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology. Comput. Medical Imaging Graph. 65: 142-151 (2018) - [i2]Ida Haeggstroem, Charles Ross Schmidtlein, Gabriele Campanella, Thomas J. Fuchs:
DeepRec: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem. CoRR abs/1804.07851 (2018) - [i1]Gabriele Campanella, Vitor Werneck Krauss Silva, Thomas J. Fuchs:
Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology. CoRR abs/1805.06983 (2018)
Coauthor Index
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