330 citations · 410 across the 12 of their papers we have counts for
12 papers
STEP: A Modular Silent Trial Engine for Operational Evaluation of Digital Pathology AI in Routine Workflow
Gabriele Campanella, Matthew Croken, Olga Lukatskaya +4
Prospective silent trials provide an important bridge between retrospective validation of artificial intelligence (AI) models and their use in clinical care by evaluating model per…
Domain-Specific Foundation Model Improves AI-Based Analysis of Neuropathology
Ruchika Verma, Shrishtee Kandoi, Robina Afzal +34
Foundation models have transformed computational pathology by providing generalizable representations from large-scale histology datasets. However, existing models are predominantl…
Predict Patient Self-reported Race from Skin Histological Images
Shengjia Chen, Ruchika Verma, Kevin Clare +6
Artificial Intelligence (AI) has demonstrated success in computational pathology (CPath) for disease detection, biomarker classification, and prognosis prediction. However, its pot…
A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models
Gabriele Campanella, Shengjia Chen, Ruchika Verma +10
The use of self-supervised learning (SSL) to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantitie…
Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective
Shengjia Chen, Gabriele Campanella, Abdulkadir Elmas +8
Recent advances in artificial intelligence (AI), in particular self-supervised learning of foundation models (FMs), are revolutionizing medical imaging and computational pathology…
Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling
Gabriele Campanella, Eugene Fluder, Jennifer Zeng +2
Artificial Intelligence (AI) has great potential to improve health outcomes by training systems on vast digitized clinical datasets. Computational Pathology, with its massive amoun…
Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images
Gabriele Campanella, Ricky Kwan, Eugene Fluder +10
Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream t…
Deep conditional transformation models for survival analysis
Gabriele Campanella, Lucas Kook, Ida Häggström +2
An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of…
H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma
Gabriele Campanella, David Ho, Ida Häggström +4
Lung cancer is the leading cause of cancer death worldwide, with lung adenocarcinoma being the most prevalent form of lung cancer. EGFR positive lung adenocarcinomas have been show…
Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary
Hassan Muhammad, Carlie S. Sigel, Gabriele Campanella +9
Unlike common cancers, such as those of the prostate and breast, tumor grading in rare cancers is difficult and largely undefined because of small sample sizes, the sheer volume of…
Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology
Gabriele Campanella, Vitor Werneck Krauss Silva, Thomas J. Fuchs
In the field of computational pathology, the use of decision support systems powered by state-of-the-art deep learning solutions has been hampered by the lack of large labeled data…
DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem
Ida Häggström, C. Ross Schmidtlein, Gabriele Campanella +1
Positron emission tomography (PET) is a cornerstone of modern radiology. The ability to detect cancer and metastases in whole body scans fundamentally changed cancer diagnosis and…