Nagesh K. Subbanna

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: This work presents an extensive validation of Nyul's approach for intensity normalization in real clinical domain where even after intensity inhomogeneity correction that accounts for scanner-specific artifacts, the MRI volumes can be affected from variations such as data heterogeneity resulting from multi-site multi-scanner acquisitions, the presence of multiple sclerosis lesions and the stage of disease progression in the brain.

TL;DR: This paper provides an accessible tutorial of the most important supervised machine learning concepts and methods, including deep learning, which are potentially the most relevant for the medical domain and depicts how machine learning models can be useful for medical applications.

TL;DR: This paper presents a fully automated hierarchical probabilistic framework for segmenting brain tumours from multispectral human brain magnetic resonance images (MRIs) using multiwindow Gabor filters and an adapted Markov Random Field (MRF) framework.

TL;DR: This work considers a general method for constructing dual Gabor elements different from the canonical dual, based on combining two Gabor frames such that the generated frame-type operator S/sub g,/spl gamma// is nonsingular.