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David S. Melnick
Researcher at Northwestern University
Publications - 8
Citations - 1461
David S. Melnick is an academic researcher from Northwestern University. The author has contributed to research in topics: Breast cancer screening & Medicine. The author has an hindex of 2, co-authored 6 publications receiving 719 citations.
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Journal ArticleDOI
International evaluation of an AI system for breast cancer screening.
Scott Mayer McKinney,Marcin Sieniek,Varun Godbole,Jonathan Godwin,Natasha Antropova,Hutan Ashrafian,Trevor Back,Mary Chesus,Greg C. Corrado,Ara Darzi,Mozziyar Etemadi,Florencia Garcia-Vicente,Fiona J. Gilbert,Mark D. Halling-Brown,Demis Hassabis,Sunny Jansen,Alan Karthikesalingam,Christopher Kelly,Dominic King,Joseph R. Ledsam,David S. Melnick,Hormuz Mostofi,Lily Peng,Joshua J. Reicher,Bernardino Romera-Paredes,Richard Sidebottom,Mustafa Suleyman,Daniel Tse,Kenneth C. Young,Jeffrey De Fauw,Shravya Shetty +30 more
TL;DR: A robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening and using a combination of AI and human inputs could help to improve screening efficiency.
Posted Content
Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases.
Zaid Nabulsi,Andrew Sellergren,Shahar Jamshy,Charles Lau,Eddie Santos,Atilla Peter Kiraly,Wenxing Ye,Jie Yang,Sahar Kazemzadeh,Jin Yu,Raju Kalidindi,Mozziyar Etemadi,Florencia Garcia Vicente,David S. Melnick,Greg S. Corrado,Lily Peng,Krish Eswaran,Daniel Tse,Neeral Beladia,Yun Liu,Po-Hsuan Cameron Chen,Shravya Shetty +21 more
TL;DR: An AI system to classify CXRs as normal or abnormal and the results suggest that the AI system trained using a large dataset containing a diverse array of CXR abnormalities generalizes to new patient populations and unseen diseases.
Journal ArticleDOI
Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19.
Zaid Nabulsi,Andrew Sellergren,Shahar Jamshy,Charles Lau,Edward Santos,Atilla Peter Kiraly,Wenxing Ye,Jie Yang,Rory Pilgrim,Sahar Kazemzadeh,Jin Yu,Sreenivasa Raju Kalidindi,Mozziyar Etemadi,Florencia Garcia-Vicente,David S. Melnick,Greg S. Corrado,Lily Peng,Krish Eswaran,Daniel Tse,Neeral Beladia,Yun Liu,Po-Hsuan Cameron Chen,Shravya Shetty +22 more
TL;DR: In this article, the authors developed and evaluated an AI system to classify chest radiography (CXR) as normal or abnormal, using a de-identified dataset of 248,445 patients from a multi-city hospital network in India.
Journal ArticleDOI
Simplified Transfer Learning for Chest Radiography Models Using Less Data.
Andrew Sellergren,Christina Chen,Zaid Nabulsi,Yuanzhen Li,Aaron Maschinot,Aaron Sarna,Jenny Huang,Charles Lau,Sreenivasa Raju Kalidindi,Mozziyar Etemadi,Florencia Garcia-Vicente,David S. Melnick,Yun Liu,Krish Eswaran,Daniel Tse,Neeral Beladia,D. Krishnan,Shravya Shetty +17 more
TL;DR: Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations.
Journal ArticleDOI
Addendum: International evaluation of an AI system for breast cancer screening.
Scott Mayer McKinney,Marcin Sieniek,Varun Godbole,Jonathan Godwin,Natasha Antropova,Hutan Ashrafian,Trevor Back,Mary Chesus,Greg S. Corrado,Ara Darzi,Mozziyar Etemadi,Florencia Garcia-Vicente,Fiona J. Gilbert,Mark D. Halling-Brown,Demis Hassabis,Sunny Jansen,Alan Karthikesalingam,Christopher Kelly,Dominic King,Joseph R. Ledsam,David S. Melnick,Hormuz Mostofi,Lily Peng,Joshua J. Reicher,Bernardino Romera-Paredes,Richard Sidebottom,Mustafa Suleyman,Daniel Tse,Kenneth C. Young,Jeffrey De Fauw,Shravya Shetty +30 more