Foundation Artificial Intelligence Models for Health Recognition Using Face Photographs (FAHR-Face)

no code implementations • 17 Jun 2025 • Fridolin Haugg, Grace Lee, John He, Leonard Nürnberg, Dennis Bontempi, Danielle S. Bitterman, Paul Catalano, Vasco Prudente, Dmitrii Glubokov, Andrew Warrington, Suraj Pai, Dirk De Ruysscher, Christian Guthier, Benjamin H. Kann, Vadim N. Gladyshev, Hugo JWL Aerts, Raymond H. Mak

Findings: For age estimation, FAHR-FaceAge had the lowest mean absolute error of 5. 1 years on public datasets, outperforming benchmark models and maintaining accuracy across the full human lifespan.

Age Estimation

Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy

no code implementations • 23 Feb 2024 • Yining Zha, Benjamin H. Kann, Zezhong Ye, Anna Zapaishchykova, John He, Shu-Hui Hsu, Jonathan E. Leeman, Kelly J. Fitzgerald, David E. Kozono, Raymond H. Mak, Hugo J. W. L. Aerts

Thus, we explore the potential of delta radiomics from on-treatment magnetic resonance (MR) imaging to track radiation dose response, inform personalized radiotherapy dosing, and predict outcomes.

Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud

1 code implementation • 7 Sep 2021 • Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi

We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions.

Bias Detection BIG-bench Machine Learning +2