16 citations · 53 across the 7 of their papers we have counts for
7 papers
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Yongchan Kwon, Eric Wu, Kevin Wu +1
Quantifying the impact of training data points is crucial for understanding the outputs of machine learning models and for improving the transparency of the AI pipeline. The influe…
Explaining medical AI performance disparities across sites with confounder Shapley value analysis
Eric Wu, Kevin Wu, James Zou
Medical AI algorithms can often experience degraded performance when evaluated on previously unseen sites. Addressing cross-site performance disparities is key to ensuring that AI…
Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms
Eric Wu, Kevin Wu, William Lotter
Data scarcity and class imbalance are two fundamental challenges in many machine learning applications to healthcare. Breast cancer classification in mammography exemplifies these…
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
William Lotter, Abdul Rahman Diab, Bryan Haslam +10
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018. To achieve earlier breast cancer detection, screening x-ray mammography is recommended by…
Validation of a deep learning mammography model in a population with low screening rates
Kevin Wu, Eric Wu, Yaping Wu +4
A key promise of AI applications in healthcare is in increasing access to quality medical care in under-served populations and emerging markets. However, deep learning models are o…
Conditional Infilling GANs for Data Augmentation in Mammogram Classification
Eric Wu, Kevin Wu, David Cox +1
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results. However, such models are constrained by the limited size of publicly availa…
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition
Kevin Wu, Eric Wu, Gabriel Kreiman
Advancements in convolutional neural networks (CNNs) have made significant strides toward achieving high performance levels on multiple object recognition tasks. While some approac…