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Over the recent years, there has been an increasing interest in large-scale classification of remote sensing images. In this context, the Inria Aerial Image Labeling Benchmark has been released online in December 2016. In this paper, we discuss the outcomes of the first year of the benchmark contest, which consisted in dense labeling of aerial images into building / not building classes, covering areas of five cities not present in the training set. We present four methods with the highest numerical accuracies, all four being convolutional neural network approaches. It is remarkable that three of these methods use the U-net architecture, which has thus proven to become a new standard in image dense labeling.

Background. Because effective prediction of survival time can be highly beneficial for the treatment of glioblastoma patients, the relationship between survival time and multiple patient characteristics has been investigated. In this paper, we investigate whether the predictive power of a survival model based on clinical patient features improves when MRI features are also included in the model. Methods. The subjects in this study were 82 glioblastoma patients for whom clinical features as well as MR imaging exams were made available by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). Twenty-six imaging features in the available MR scans were assessed by radiologists from the TCGA Glioma Phenotype Research Group. We used multivariate Cox proportional hazards regression to construct 2 survival models: one that used 3 clinical features (age, gender, and KPS) as the covariates and 1 that used both the imaging features and the clinical features as the covariates. Then, we used 2 measures to compare the predictive performance of these 2 models: area under the receiver operating characteristic curve for the 1-year survival threshold and overall concordance index. To eliminate any positive performance estimation bias, we used leave-one-out cross-validation. Results. The performance of the model based on both clinical and imaging features was higher than the performance of the model based on only the clinical features, in terms of both area under the receiver operating characteristic curve (P , .01) and the overall concordance index (P , .01). Conclusions. Imaging features assessed using a controlled lexicon have additional predictive value compared with clinical features when predicting survival time in glioblastoma patients.Keywords: glioblastoma, MRI, proportional hazards, survival analysis, VASARI. G lioblastoma (GBM) is the most commonly occurring type of malignant primary brain tumor and the second most common type of primary brain tumor in general.1 It is characterized by very poor survival rates: a 1-year survival rate of 35.2% and a 5-year survival rate of only 4.7%. 1Accurate prognosis for individual patients could be of high benefit to them, and thus multiple studies have been published examining the impact of various factors on time to death. Lacroix et al 2 have shown that a high (≥98%) extent of tumor resection gives a significant survival advantage compared with a low (,98%) extent of resection. The dependence of survival on complete resection of the enhancing tumor was further confirmed by Stummer et al. 3 Regarding clinical features, it has been demonstrated that age 2,4 and Karnofsky Performance Status (KPS) 2,4,5,6 are significant predictors of survival. Multiple recent studies focus on genomic predictors of survival in GBM patients. One among the most prominent studies is that of Verhaak et al,7 who found a gene expression-based classification for GBM patients that relates well to their clinical outcomes.Although notably less attention has been given to the predictive value of p...