2 citations · 3 across the 4 of their papers we have counts for
4 papers
GrInAdapt: Scaling Retinal Vessel Structural Map Segmentation Through Grounding, Integrating and Adapting Multi-device, Multi-site, and Multi-modal Fundus Domains
Zixuan Liu, Aaron Honjaya, Yuekai Xu +6
Retinal vessel segmentation is critical for diagnosing ocular conditions, yet current deep learning methods are limited by modality-specific challenges and significant distribution…
OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation
Zixuan Liu, Hanwen Xu, Addie Woicik +9
We present OCTCube-M, a 3D OCT-based multi-modal foundation model for jointly analyzing OCT and en face images. OCTCube-M first developed OCTCube, a 3D foundation model pre-trained…
T-Rex: Text-assisted Retrosynthesis Prediction
Yifeng Liu, Hanwen Xu, Tangqi Fang +5
As a fundamental task in computational chemistry, retrosynthesis prediction aims to identify a set of reactants to synthesize a target molecule. Existing template-free approaches o…
ChiMera: Learning with noisy labels by contrasting mixed-up augmentations
Zixuan Liu, Xin Zhang, Junjun He +5
Learning with noisy labels has been studied to address incorrect label annotations in real-world applications. In this paper, we present ChiMera, a two-stage learning-from-noisy-la…