330 citations · 410 across the 12 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…