96 citations · 158 across the 7 of their papers we have counts for
7 papers
Advancing Multimodal Medical Capabilities of Gemini
Lin Yang, Shawn Xu, Andrew Sellergren +44
Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Buil…
Capabilities of Gemini Models in Medicine
Khaled Saab, Tao Tu, Wei-Hung Weng +64
Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of…
Joint Debiased Representation and Image Clustering Learning with Self-Supervision
Shunjie-Fabian Zheng, JaeEun Nam, Emilio Dorigatti +3
Contrastive learning is among the most successful methods for visual representation learning, and its performance can be further improved by jointly performing clustering on the le…
Big Self-Supervised Models Advance Medical Image Classification
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan +9
Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attentio…
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
Zili Yi, Qiang Tang, Shekoofeh Azizi +2
Recently data-driven image inpainting methods have made inspiring progress, impacting fundamental image editing tasks such as object removal and damaged image repairing. These meth…
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations
Saeid Asgari Taghanaki, Kumar Abhishek, Shekoofeh Azizi +1
The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, we propose a non-linea…