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.
In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers.
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.
no code implementations • 23 Dec 2019 • William Lotter, Abdul Rahman Diab, Bryan Haslam, Jiye G. Kim, Giorgia Grisot, Eric Wu, Kevin Wu, Jorge Onieva Onieva, Jerrold L. Boxerman, Meiyun Wang, Mack Bandler, Gopal Vijayaraghavan, A. Gregory Sorensen
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018.
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.
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.
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results.
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.