Gustavo Carneiro

TL;DR: In this article, the authors assess the utility of several possible techniques for measuring and describing hidden stratification effects, and characterize these effects on multiple medical imaging datasets, and find evidence that Hidden stratification can occur in unidentified imaging subsets with low prevalence, low label quality, subtle distinguishing features, or spurious correlates, and that it can result in relative performance differences on clinically important subsets.

TL;DR: This work shows that, under the database centric probabilistic model, optimal annotation and retrieval can be implemented with algorithms that are conceptually simple, computationally efficient, and do not require prior semantic segmentation of training images.

TL;DR: A snapshot of this fast growing field specifically for mammography, cardiovascular, and microscopy image analysis is provided, briefly explain the popular deep neural networks and summarize current deep learning achievements in various tasks such as detection, segmentation, and classification in these heterogeneous imaging modalities.

TL;DR: In this paper, the authors use semi-local spatial constraints to reduce the number of hypotheses that must be verified and also reduce the false positives present in each of these hypotheses, which allows for a greater range of shape deformations.

TL;DR: In this paper, a structured support vector machine (SSVM) was used to combine different types of potential functions, including one that classifies image regions using deep learning, for segmentation of breast masses from mammograms.