1 code implementation • 3 Apr 2024 • Hui Xue, Sarah Hooper, Azaan Rehman, Iain Pierce, Thomas Treibel, Rhodri Davies, W Patricia Bandettini, Rajiv Ramasawmy, Ahsan Javed, Zheren Zhu, Yang Yang, James Moon, Adrienne Campbell, Peter Kellman
The ability to recover MRI signal from noise is key to achieve fast acquisition, accurate quantification, and high image quality.
1 code implementation • ICCV 2023 • Maya Varma, Jean-Benoit Delbrouck, Sarah Hooper, Akshay Chaudhari, Curtis Langlotz
The first key contribution of this work is to demonstrate through systematic evaluations that as the pairwise complexity of the training dataset increases, standard VLMs struggle to learn region-attribute relationships, exhibiting performance degradations of up to 37% on retrieval tasks.
no code implementations • ICLR 2021 • Sarah Hooper, Michael Wornow, Ying Hang Seah, Peter Kellman, Hui Xue, Frederic Sala, Curtis Langlotz, Christopher Re
We propose a framework that fuses limited label learning and weak supervision for segmentation tasks, enabling users to train high-performing segmentation CNNs with very few hand-labeled training points.