Artificial intelligence (AI) has great potential in medical imaging to augment the clinician as a virtual radiology assistant (vRA) through enriching information and providing clinical decision support. Deep learning is a type of AI that has shown promise in performance for Computer Aided Diagnosis (CAD) tasks. A current barrier to implementing deep learning for clinical CAD tasks in radiology is that it requires a training set to be representative and as large as possible in order to generalize appropriately and achieve high accuracy predictions. There is a lack of available, reliable, discretized and annotated labels for computer vision research in radiology despite the abundance of diagnostic imaging examinations performed in routine clinical practice. Furthermore, the process to create reliable labels is tedious, time consuming and requires expertise in clinical radiology. We present an Active Semi-supervised Expectation Maximization (ASEM) learning model for training a Convolutional Neural Network (CNN) for lung cancer screening using Computed Tomography (CT) imaging examinations. Our learning model is novel since it combines Semi-supervised learning via the Expectation-Maximization (EM) algorithm with Active learning via Bayesian experimental design for use with 3D CNNs for lung cancer screening. ASEM simultaneously infers image labels as a latent variable, while predicting which images, if additionally labeled, are likely to improve classification accuracy. The performance of this model has been evaluated using three publicly available chest CT datasets: Kaggle2017, NLST, and LIDC-IDRI. Our experiments showed that ASEM-CAD can identify suspicious lung nodules and detect lung cancer cases with an accuracy of 92% (Kaggle17), 93% (NLST), and 73% (LIDC) and Area Under Curve (AUC) of 0.94 (Kaggle), 0.88 (NLST), and 0.81 (LIDC). These performance numbers are comparable to fully supervised training, but use only slightly more than 50% of the training data labels .
Increased greenhouse gasses reduce the transmission of Outgoing Longwave Radiation (OLR) to space along spectral absorption lines eventually causing the Earth’s temperature to rise in order to preserve energy equilibrium. This greenhouse forcing effect can be directly observed in the Outgoing Longwave Spectra (OLS) from space-borne infrared instruments with sufficiently high resolving power <sup>3, 8</sup>. In 2001, Harries et. al observed significant increases in greenhouse forcings by direct inter-comparison of the IRIS spectra 1970 and the IMG spectra 1997<sup>8</sup>. We have extended this effort by measuring the annual rate of change of AIRS all-sky Outgoing Longwave Spectra (OLS) with respect to greenhouse forcings. Our calculations make use of a 2°x2° degree monthly gridded Brightness Temperature (BT) product. Decadal trends for AIRS spectra from 2002-2012 indicate continued decrease of -0.06 K/yr in the trend of CO2 BT (700cm<sup>-1</sup> and 2250cm<sup>-1</sup>), a decrease of -0.04 K/yr of O3 BT (1050 cm<sup>-1</sup>), and a decrease of -0.03 K/yr of the CH4 BT (1300cm<sup>-1</sup>). Observed decreases in BT trends are expected due to ten years of increased greenhouse gasses even though global surface temperatures have not risen substantially over the last decade.