2 code implementations • 5 Jan 2024 • Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju, Dwarikanath Mahapatra, Brigid Betz-Stablein, Victoria Mar, Monika Janda, Peter Soyer, ZongYuan Ge
To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG).
no code implementations • 2 Nov 2023 • Deval Mehta, Brigid Betz-Stablein, Toan D Nguyen, Yaniv Gal, Adrian Bowling, Martin Haskett, Maithili Sashindranath, Paul Bonnington, Victoria Mar, H Peter Soyer, ZongYuan Ge
For a clinical image, our model generates three outputs: a hierarchical prediction, an alert for out-of-distribution images, and a recommendation for dermoscopy if clinical image alone is insufficient for diagnosis.
1 code implementation • 4 Apr 2023 • Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju, Dwarikanath Mahapatrainst, Victoria Mar, Monika Janda, Peter Soyer, ZongYuan Ge
Concretely, EPVT leverages a set of domain prompts, each of which plays as a domain expert, to capture domain-specific knowledge; and a shared prompt for general knowledge over the entire dataset.
no code implementations • 13 Sep 2022 • Zhen Yu, Toan Nguyen, Yaniv Gal, Lie Ju, Shekhar S. Chandra, Lei Zhang, Paul Bonnington, Victoria Mar, Zhiyong Wang, ZongYuan Ge
Accordingly, the learned prototypes preserve the semantic class relations in the embedding space and we can predict the label of an image by assigning its feature to the nearest hyperbolic class prototype.
no code implementations • 12 Oct 2021 • Zhen Yu, Jennifer Nguyen, Toan D Nguyen, John Kelly, Catriona Mclean, Paul Bonnington, Lei Zhang, Victoria Mar, ZongYuan Ge
In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images.
no code implementations • 19 Jun 2020 • Zhen Yu, Jennifer Nguyen, Xiaojun Chang, John Kelly, Catriona Mclean, Lei Zhang, Victoria Mar, ZongYuan Ge
Existing studies for automated melanoma diagnosis are based on single-time point images of lesions.