activity
20202024
most citedInterpretable Survival Prediction for Colorectal Cancer using Deep Learning

233 citations · 339 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024★ 30 cited

Advancing Multimodal Medical Capabilities of Gemini

Lin Yang, Shawn Xu, Andrew Sellergren +44

Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Buil…

cs.CV2023★ 18 cited

ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders

Shawn Xu, Lin Yang, Christopher Kelly +25

In this work, we present an approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or grafted onto…

cs.LG2022

Enabling faster and more reliable sonographic assessment of gestational age through machine learning

Chace Lee, Angelica Willis, Christina Chen +8

Fetal ultrasounds are an essential part of prenatal care and can be used to estimate gestational age (GA). Accurate GA assessment is important for providing appropriate prenatal ca…

cs.LG2022★ 2 cited

AI system for fetal ultrasound in low-resource settings

Ryan G. Gomes, Bellington Vwalika, Chace Lee +26

Despite considerable progress in maternal healthcare, maternal and perinatal deaths remain high in low-to-middle income countries. Fetal ultrasound is an important component of ant…

eess.IV2021★ 1 cited

Deep learning for detecting pulmonary tuberculosis via chest radiography: an international study across 10 countries

Sahar Kazemzadeh, Jin Yu, Shahar Jamshy +21

Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO recommends chest radiographs (CXRs) for TB screening, the limited availability of CXR interpretation is a bar…

eess.IV2020★ 233 cited

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…

eess.IV2020★ 55 cited

Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases

Zaid Nabulsi, Andrew Sellergren, Shahar Jamshy +20

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific…