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20172023
most citedPredicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning

1.9k citations · 6.6k across the 25 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

q-bio.QM2019★ 283 cited

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…

eess.IV2019★ 787 cited

A deep learning system for differential diagnosis of skin diseases

Yuan Liu, Ayush Jain, Clara Eng +19

Skin conditions affect an estimated 1.9 billion people worldwide. A shortage of dermatologists causes long wait times and leads patients to seek dermatologic care from general prac…

cs.CV2019★ 236 cited

Detecting Anemia from Retinal Fundus Images

Akinori Mitani, Yun Liu, Abigail Huang +5

Despite its high prevalence, anemia is often undetected due to the invasiveness and cost of screening and diagnostic tests. Though some non-invasive approaches have been developed,…

cs.CV2019★ 127 cited

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…

cs.CV2019★ 101 cited

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).…