Çağatay Demiralp

TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.

TL;DR: The discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest.

TL;DR: Expert feedback from doctors studying changes in white-matter structures after gamma-knife capsulotomy and preoperative planning for brain tumor surgery shows that streamtubes correlate well with major neural structures, the 2D section and geometric landmarks are important in understanding the visualization, and the stereo and interactivity from the virtual environment aid inUnderstanding the complex geometric models.

TL;DR: Sherlock is introduced, a multi-input deep neural network for detecting semantic types that achieves a support-weighted F$_1 score of $0.89, exceeding that of machine learning baselines, dictionary and regular expression benchmarks, and the consensus of crowdsourced annotations.

TL;DR: This work introduces perceptual kernels: distance matrices derived from aggregate perceptual judgments, which represent perceptual differences between and within visual variables in a reusable form that is directly applicable to visualization evaluation and automated design.