Adaptive statistical pattern classifiers for remotely sensed data

A technique for the adaptive estimation of nonstationary statistics necessary for Bayesian classification is developed. The basic approach to the adaptive estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest and (2) a projection of the parameters in time or position. A divergence criterion is developed to monitor algorithm performance. Comparative results of adaptive and nonadaptive classifier tests are presented for simulated four dimensional spectral scan data

Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease

We propose an automatic method using dilated convolutional neural networks (CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR (CMR) of patients with congenital heart disease (CHD). Ten training and ten test CMR scans cropped to an ROI around the heart were provided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptive field of 131x131 voxels was trained for myocardium and blood pool segmentation in axial, sagittal and coronal image slices. Performance was evaluated within the HVSMR challenge. Automatic segmentation of the test scans resulted in Dice indices of 0.80$\pm$0.06 and 0.93$\pm$0.02, average distances to boundaries of 0.96$\pm$0.31 and 0.89$\pm$0.24 mm, and Hausdorff distances of 6.13$\pm$3.76 and 7.07$\pm$3.01 mm for the myocardium and blood pool, respectively. Segmentation took 41.5$\pm$14.7 s per scan. In conclusion, dilated CNNs trained on a small set of CMR images of CHD patients showing large anatomical variability provide accurate myocardium and blood pool segmentations

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the interme- diate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incom- plete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Com- pared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.Comment: Presented at the Deep Learning in Medical Image Analysis Workshop, MICCAI 201

S12RS SGB No. 8 (New Committees)

A BILL To amend the Senate Rules of Orde

Neutrino-nucleus cross section within the extended factorization scheme

The factorization scheme, based on the impulse approximation and the spectral function formalism, has been recently generalized to allow the description of electromagnetic nuclear interactions driven by two-nucleon currents. We have extended this framework to the case of weakly charged and neutral currents, and carried out calculations of the double-differential neutrino-carbon and neutrino-oxygen cross sections using two different models of the target spectral functions. The results, showing a moderate dependence on the input spectral function, confirm that our approach provides a consistent treatment of all reaction mechanisms contributing to the signals detected by accelerator-based neutrino experiments

On Adaptive Emergence of Trust Behavior in the Game of Stag Hunt

We study the emergence of trust behavior at both the individual and the population levels. At the individual level, in contrast to prior research that views trust as fixed traits, we model the emergence of trust or cooperation as a result of trial and error learning by a computer algorithm borrowed from the field of artificial intelligence (Watkins 1989). We show that trust can indeed arise as a result of trial and error learning. Emergence of trust at the population level is modeled by a grid-world consisting of cells of individual agents, a technique known as spatialization in evolutionary game theory. We show that, under a wide range of assumptions, trusting individuals tend to take over the population and trust becomes a systematic property. At both individual and population levels, therefore, we argue that trust behaviors will often emerge as a result of learning