In-context learning (ICL) with Large Vision Models (LVMs) presents a promising avenue in medical image segmentation by reducing the reliance on extensive labeling. However, the ICL performance of LVMs highly depends on the choices of visual ...

The energy industry is undergoing significant transformations as it strives to achieve net-zero emissions and future-proof its infrastructure, where every participant in the power grid has the potential to both consume and produce energy resources. ...

The collaborative nature of federated learning (FL) poses a major threat in the form of manipulation of local training data and local updates, known as the Byzantine poisoning attack. To address this issue, many Byzantine-robust aggregation rules (<...

The Poisoning Membership Inference Attack (PMIA) is a newly emerging privacy attack that poses a significant threat to federated learning (FL). An adversary conducts data poisoning (i.e., performing adversarial manipulations on training examples) to ...

Manipulation of local training data and local updates, i.e., the Byzantine poisoning attack, is the main threat arising from the collaborative nature of the federated learning (FL) paradigm. Many Byzantine-robust aggregation algorithms (AGRs) have been ...

Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging. However, the pure data-driven nature of deep learning models may limit the model generalizability and application scope. Here we establish ...

• We propose a geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction (GIIR) by integrating geometric priors of the ...

Deepening our understanding of human health is more important than ever before for addressing real-world challenges in biomedicine and healthcare, especially with the recent pandemic. My research focuses on AI in medicine, to develop efficient ML ...

This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale, 2) consist of hand-drawn bboxes, which are unavailable under realistic settings, 3) have only one ...