52 citations · 159 across the 15 of their papers we have counts for
8 papers · 1 filter
JASPR: Joint Spatial Representation learning of histology and spatial genomics for improved virtual genomic screening and clinical prognostication
Marija Pizurica, Eric Zimmermann, Neil Tenenholtz +5
Recent studies have shown that spatial properties of tumors are critical for understanding disease biology and predicting patient outcomes. These spatial properties are increasingl…
Mixed Magnification Aggregation for Generalizable Region-Level Representations in Computational Pathology
Eric Zimmermann, Julian Viret, Michal Zelechowski +7
In recent years, a standard computational pathology workflow has emerged where whole slide images are cropped into tiles, these tiles are processed using a foundation model, and ta…
PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue
Eugene Vorontsov, George Shaikovski, Adam Casson +16
Recent rapid progress in the field of computational pathology has been enabled by foundation models. These models are beginning to move beyond encoding image patches towards whole-…
Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology
Eric Zimmermann, Eugene Vorontsov, Julian Viret +11
Foundation models are rapidly being developed for computational pathology applications. However, it remains an open question which factors are most important for downstream perform…
Adapting Self-Supervised Learning for Computational Pathology
Eric Zimmermann, Neil Tenenholtz, James Hall +8
Self-supervised learning (SSL) has emerged as a key technique for training networks that can generalize well to diverse tasks without task-specific supervision. This property makes…
Improving mitosis detection on histopathology images using large vision-language models
Ruiwen Ding, James Hall, Neil Tenenholtz +1
In certain types of cancerous tissue, mitotic count has been shown to be associated with tumor proliferation, poor prognosis, and therapeutic resistance. Due to the high inter-rate…
Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks
Christopher P. Bridge, Michael Rosenthal, Bradley Wright +15
The amounts of muscle and fat in a person's body, known as body composition, are correlated with cancer risks, cancer survival, and cardiovascular risk. The current gold standard f…
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks
Hoo-Chang Shin, Neil A Tenenholtz, Jameson K Rogers +5
Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces…