no code implementations • 20 Oct 2023 • Jeremy Lai, Faruk Ahmed, Supriya Vijay, Tiam Jaroensri, Jessica Loo, Saurabh Vyawahare, Saloni Agarwal, Fayaz Jamil, Yossi Matias, Greg S. Corrado, Dale R. Webster, Jonathan Krause, Yun Liu, Po-Hsuan Cameron Chen, Ellery Wulczyn, David F. Steiner
Foundation models in histopathology that learn general representations across a wide range of tissue types, diagnoses, and magnifications offer the potential to reduce the data, compute, and technical expertise necessary to develop task-specific deep learning models with the required level of model performance.
no code implementations • 19 Jul 2022 • Boris Babenko, Ilana Traynis, Christina Chen, Preeti Singh, Akib Uddin, Jorge Cuadros, Lauren P. Daskivich, April Y. Maa, Ramasamy Kim, Eugene Yu-Chuan Kang, Yossi Matias, Greg S. Corrado, Lily Peng, Dale R. Webster, Christopher Semturs, Jonathan Krause, Avinash V. Varadarajan, Naama Hammel, Yun Liu
On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7. 3-13. 2%.
For instance, Noisy Cross-Validation splits the training data into halves, and has been shown to identify low-quality labels in computer vision tasks; but it has not been applied to medical imaging tasks specifically.
no code implementations • 21 Dec 2018 • Sonia Phene, R. Carter Dunn, Naama Hammel, Yun Liu, Jonathan Krause, Naho Kitade, Mike Schaekermann, Rory Sayres, Derek J. Wu, Ashish Bora, Christopher Semturs, Anita Misra, Abigail E. Huang, Arielle Spitze, Felipe A. Medeiros, April Y. Maa, Monica Gandhi, Greg S. Corrado, Lily Peng, Dale R. Webster
An algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers.
no code implementations • 18 Oct 2018 • Paisan Raumviboonsuk, Jonathan Krause, Peranut Chotcomwongse, Rory Sayres, Rajiv Raman, Kasumi Widner, Bilson J L Campana, Sonia Phene, Kornwipa Hemarat, Mongkol Tadarati, Sukhum Silpa-Acha, Jirawut Limwattanayingyong, Chetan Rao, Oscar Kuruvilla, Jesse Jung, Jeffrey Tan, Surapong Orprayoon, Chawawat Kangwanwongpaisan, Ramase Sukulmalpaiboon, Chainarong Luengchaichawang, Jitumporn Fuangkaew, Pipat Kongsap, Lamyong Chualinpha, Sarawuth Saree, Srirat Kawinpanitan, Korntip Mitvongsa, Siriporn Lawanasakol, Chaiyasit Thepchatri, Lalita Wongpichedchai, Greg S. Corrado, Lily Peng, Dale R. Webster
Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-level accuracy.
We show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection.
Diabetic retinopathy (DR) and diabetic macular edema are common complications of diabetes which can lead to vision loss.
In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data.
We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts.
The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors.
Recent progress on image captioning has made it possible to generate novel sentences describing images in natural language, but compressing an image into a single sentence can describe visual content in only coarse detail.
Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes.
Ranked #4 on Fine-Grained Image Classification on CUB-200-2011 (using extra training data)
Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories.
12 code implementations • 1 Sep 2014 • Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.