Plots Unlock Time-Series Understanding in Multimodal Models

no code implementations • 3 Oct 2024 • Mayank Daswani, Mathias M. J. Bellaiche, Marc Wilson, Desislav Ivanov, Mikhail Papkov, Eva Schnider, Jing Tang, Kay Lamerigts, Gabriela Botea, Michael A. Sanchez, Yojan Patel, Shruthi Prabhakara, Shravya Shetty, Umesh Telang

While multimodal foundation models can now natively work with data beyond text, they remain underutilized in analyzing the considerable amounts of multi-dimensional time-series data in fields like healthcare, finance, and social sciences, representing a missed opportunity for richer, data-driven insights.

Activity Recognition Time Series

MINT: A wrapper to make multi-modal and multi-image AI models interactive

no code implementations • 22 Jan 2024 • Jan Freyberg, Abhijit Guha Roy, Terry Spitz, Beverly Freeman, Mike Schaekermann, Patricia Strachan, Eva Schnider, Renee Wong, Dale R Webster, Alan Karthikesalingam, Yun Liu, Krishnamurthy Dvijotham, Umesh Telang

In this paper we tackle a more subtle challenge: doctors take a targeted medical history to obtain only the most pertinent pieces of information; how do we enable AI to do the same?

Disease Prediction

Evaluating AI systems under uncertain ground truth: a case study in dermatology

1 code implementation • 5 Jul 2023 • David Stutz, Ali Taylan Cemgil, Abhijit Guha Roy, Tatiana Matejovicova, Melih Barsbey, Patricia Strachan, Mike Schaekermann, Jan Freyberg, Rajeev Rikhye, Beverly Freeman, Javier Perez Matos, Umesh Telang, Dale R. Webster, YuAn Liu, Greg S. Corrado, Yossi Matias, Pushmeet Kohli, Yun Liu, Arnaud Doucet, Alan Karthikesalingam

In contrast, we propose a framework where aggregation is done using a statistical model.

Robust and Efficient Medical Imaging with Self-Supervision

3 code implementations • 19 May 2022 • Shekoofeh Azizi, Laura Culp, Jan Freyberg, Basil Mustafa, Sebastien Baur, Simon Kornblith, Ting Chen, Patricia MacWilliams, S. Sara Mahdavi, Ellery Wulczyn, Boris Babenko, Megan Wilson, Aaron Loh, Po-Hsuan Cameron Chen, YuAn Liu, Pinal Bavishi, Scott Mayer McKinney, Jim Winkens, Abhijit Guha Roy, Zach Beaver, Fiona Ryan, Justin Krogue, Mozziyar Etemadi, Umesh Telang, Yun Liu, Lily Peng, Greg S. Corrado, Dale R. Webster, David Fleet, Geoffrey Hinton, Neil Houlsby, Alan Karthikesalingam, Mohammad Norouzi, Vivek Natarajan

These results suggest that REMEDIS can significantly accelerate the life-cycle of medical imaging AI development thereby presenting an important step forward for medical imaging AI to deliver broad impact.

Representation Learning Self-Supervised Learning +1

Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions

no code implementations • 8 Apr 2021 • Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, YuAn Liu, Zach Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Greg S. Corrado, Umesh Telang, Yun Liu, Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, Jim Winkens

We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier.

Outlier Detection Out of Distribution (OOD) Detection +1

Supervised Transfer Learning at Scale for Medical Imaging

no code implementations • 14 Jan 2021 • Basil Mustafa, Aaron Loh, Jan Freyberg, Patricia MacWilliams, Megan Wilson, Scott Mayer McKinney, Marcin Sieniek, Jim Winkens, YuAn Liu, Peggy Bui, Shruthi Prabhakara, Umesh Telang, Alan Karthikesalingam, Neil Houlsby, Vivek Natarajan

However, for medical imaging, the value of transfer learning is less clear.

Fairness Out-of-Distribution Generalization +1