no code implementations • 16 Feb 2024 • Gautam Rajendrakumar Gare, Tom Fox, Beam Chansangavej, Amita Krishnan, Ricardo Luis Rodriguez, Bennett P deBoisblanc, Deva Kannan Ramanan, John Michael Galeotti
Accurate and interpretable diagnostic models are crucial in the safety-critical field of medicine.
no code implementations • 16 Jun 2022 • Gautam Rajendrakumar Gare, Tom Fox, Pete Lowery, Kevin Zamora, Hai V. Tran, Laura Hutchins, David Montgomery, Amita Krishnan, Deva Kannan Ramanan, Ricardo Luis Rodriguez, Bennett P deBoisblanc, John Michael Galeotti
We propose to decouple feature learning from downstream lung ultrasound tasks by introducing an auxiliary pre-task of visual biomarker classification.
no code implementations • 25 Jan 2022 • Gautam Rajendrakumar Gare, Andrew Schoenling, Vipin Philip, Hai V Tran, Bennett P deBoisblanc, Ricardo Luis Rodriguez, John Michael Galeotti
Our segmentation-based models perform better classification when using pretrained segmentation weights, with the dense-label pretrained U-Net performing the best.
no code implementations • 19 Jan 2022 • Gautam Rajendrakumar Gare, Wanwen Chen, Alex Ling Yu Hung, Edward Chen, Hai V. Tran, Tom Fox, Pete Lowery, Kevin Zamora, Bennett P deBoisblanc, Ricardo Luis Rodriguez, John Michael Galeotti
In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis.
3 code implementations • 18 Jan 2022 • Gautam Rajendrakumar Gare, Hai V. Tran, Bennett P deBoisblanc, Ricardo Luis Rodriguez, John Michael Galeotti
Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis.
no code implementations • 25 Mar 2021 • Gautam Rajendrakumar Gare, John Michael Galeotti
We view this type of clustering as valuable information and exploit it with our novel projective loss functions.