509 citations · 1.3k across the 6 of their papers we have counts for
6 papers
Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading
Ellery Wulczyn, Kunal Nagpal, Matthew Symonds +20
Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial i…
Interpretable Survival Prediction for Colorectal Cancer using Deep Learning
Ellery Wulczyn, David F. Steiner, Melissa Moran +20
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) f…
Deep learning-based survival prediction for multiple cancer types using histopathology images
Ellery Wulczyn, David F. Steiner, Zhaoyang Xu +7
Prognostic information at diagnosis has important implications for cancer treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and c…
Similar Image Search for Histopathology: SMILY
Narayan Hegde, Jason D. Hipp, Yun Liu +11
The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Thou…
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection
Timo Kohlberger, Yun Liu, Melissa Moran +6
Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF).…
Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer
Kunal Nagpal, Davis Foote, Yun Liu +17
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scorin…