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Found 9 papers, 3 papers with code
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
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.
GPT detectors are biased against non-native English writers
In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers.
Explaining medical AI performance disparities across sites with confounder Shapley value analysis
Medical AI algorithms can often experience degraded performance when evaluated on previously unseen sites.
Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms
Breast cancer classification in mammography exemplifies these challenges, with a malignancy rate of around 0. 5% in a screening population, which is compounded by the relatively small size of lesions (~1% of the image) in malignant cases.
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018.
Validation of a deep learning mammography model in a population with low screening rates
We specifically explore how a deep learning algorithm trained on screening mammograms from the US and UK generalizes to mammograms collected at a hospital in China, where screening is not widely implemented.
Residual Attention based Network for Hand Bone Age Assessment
The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.
Conditional Infilling GANs for Data Augmentation in Mammogram Classification
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results.
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition
We use a biologically inspired two-part convolutional neural network ('GistNet') that models the fovea and periphery to provide a proof-of-principle demonstration that computational object recognition can significantly benefit from the gist of the scene as contextual information.
