2 citations · 3 across the 5 of their papers we have counts for
5 papers
MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models
Hyunjae Kim, Dain Kim, Pan Xiao +25
Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundation models is constrained by…
VOLMO: Versatile and Open Large Models for Ophthalmology
Zhenyue Qin, Younjoon Chung, Elijah Lee +16
Vision impairment affects millions globally, and early detection is critical to preventing irreversible vision loss. Ophthalmology workflows require clinicians to integrate medical…
U-Harmony: Enhancing Joint Training for Segmentation Models with Universal Harmonization
Weiwei Ma, Xiaobing Yu, Peijie Qiu +7
In clinical practice, medical segmentation datasets are often limited and heterogeneous, with variations in modalities, protocols, and anatomical targets across institutions. Exist…
SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification
Peijie Qiu, Pan Xiao, Wenhui Zhu +2
Multiple Instance Learning (MIL) has been widely used in weakly supervised whole slide image (WSI) classification. Typical MIL methods include a feature embedding part, which embed…
SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA
Pan Xiao, Peijie Qiu, Sungmin Ha +3
Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. Several variants of variational autoencoder…