Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology. - Gabriele Campanella; Arjun R. Rajanna; Lorraine Corsale; Peter J. Schüffler; Yukako Yagi; Thomas J. Fuchs (Comput. Medical Imaging Graph., 2018)

DeepRec: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem. - Ida Haeggstroem; Charles Ross Schmidtlein; Gabriele Campanella; Thomas J. Fuchs (CoRR, 2018)

Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology. - Gabriele Campanella; Vitor Werneck Krauss Silva; Thomas J. Fuchs (CoRR, 2018)

Unsupervised Subtyping of Cholangiocarcinoma Using a Deep Clustering Convolutional Autoencoder. - 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 (MICCAI (1), 2019)

Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary. - 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 (CoRR, 2019)

DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem. - Ida Häggström; Charles Ross Schmidtlein; Gabriele Campanella; Thomas J. Fuchs (Medical Image Anal., 2019)

Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning. - Chensu Xie; Hassan Muhammad; Chad M. Vanderbilt; Raul Caso; Dig Vijay Kumar Yarlagadda; Gabriele Campanella; Thomas J. Fuchs (MIDL, 2020)

H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma. - Gabriele Campanella; David Joon Ho; Ida Häggström; Anton S. Becker; Jason Chang; Chad M. Vanderbilt; Thomas J. Fuchs (CoRR, 2022)

Deep conditional transformation models for survival analysis. - Gabriele Campanella; Lucas Kook; Ida Häggström; Torsten Hothorn; Thomas J. Fuchs (CoRR, 2022)

Computational Pathology at Health System Scale - Self-Supervised Foundation Models from Three Billion Images. - 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; et al. (CoRR, 2023)

Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective. - Shengjia Chen; Gabriele Campanella; Abdulkadir Elmas; Aryeh Stock; Jennifer Zeng; Alexandros D. Polydorides; Adam J. Schoenfeld; Kuan-Lin Huang; Jane Houldsworth; Chad Vanderbilt; Thomas J. Fuchs (COMPAY@MICCAI, 2024)

Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling. - Gabriele Campanella; Eugene Fluder; Jennifer Zeng; Chad M. Vanderbilt; Thomas J. Fuchs (CoRR, 2024)

A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models. - 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; et al. (CoRR, 2024)

Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology. - Boqi Chen; Cédric Vincent-Cuaz; Lydia A. Schoenpflug; Manuel Madeira; Lisa Fournier; Vaishnavi Subramanian; Sonali Andani; Samuel Ruipérez-Campillo; Julia E. Vogt; Raphaëlle Luisier; Dorina Thanou; Viktor H. Koelzer; et al. (CoRR, 2025)

Single GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis. - Neeraj Kumar; Swaraj Nanda; Siddharth Singi; Jamal Benhamida; David Kim; Jie-Fu Chen; Amir Momeni-Boroujeni; Gregory M. Goldgof; Gabriele Campanella; Chad Vanderbilt (CoRR, 2025)

Predict Patient Self-reported Race from Skin Histological Images. - Shengjia Chen; Ruchika Verma; Kevin Clare; Jannes Jegminat; Eugenia Alleva; Kuan-Lin Huang; Brandon Veremis; Thomas J. Fuchs; Gabriele Campanella (CoRR, 2025)