CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation

no code implementations • 22 Jan 2024 • Zhihong Chen, Maya Varma, Jean-Benoit Delbrouck, Magdalini Paschali, Louis Blankemeier, Dave Van Veen, Jeya Maria Jose Valanarasu, Alaa Youssef, Joseph Paul Cohen, Eduardo Pontes Reis, Emily B. Tsai, Andrew Johnston, Cameron Olsen, Tanishq Mathew Abraham, Sergios Gatidis, Akshay S. Chaudhari, Curtis Langlotz

However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation.

Benchmarking Fairness +2

A Boosting Approach to Constructing an Ensemble Stack

no code implementations • 28 Nov 2022 • Zhilei Zhou, Ziyu Qiu, Brad Niblett, Andrew Johnston, Jeffrey Schwartzentruber, Nur Zincir-Heywood, Malcolm Heywood

An approach to evolutionary ensemble learning for classification is proposed in which boosting is used to construct a stack of programs.

Benchmarking Ensemble Learning

Machine Learning for a Sustainable Energy Future

no code implementations • 19 Oct 2022 • Zhenpeng Yao, Yanwei Lum, Andrew Johnston, Luis Martin Mejia-Mendoza, Xin Zhou, Yonggang Wen, Alan Aspuru-Guzik, Edward H. Sargent, Zhi Wei Seh

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient harvesting, storage, conversion, and management of renewable energy.

Management