1 Tight-Binding Modeling of Charge Migration in DNA Devices

Within the class of biopolymers, DNA is expected to play an outstanding role in molecular electronics [1]. This is mainly due to its unique self-assembling and self-recognition properties which are essential for its performance as carrier of the genetic code. It is the long-standing hope of many scientists tha

Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

We present several deep learning models for assessing the morphometric delity of deep grey matter region models extracted from brain MRI. We test three dierent convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks