Holistic Multi-View Building Analysis in the Wild with Projection Pooling

We address six different classification tasks related to fine-grained building attributes: construction type, number of floors, pitch and geometry of the roof, facade material, and occupancy class. Tackling such a remote building analysis problem became possible only recently due to growing large-scale datasets of urban scenes. To this end, we introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings. These photos are further assembled, together with the geometric metadata. The dataset showcases various real-world challenges, such as occlusions, blur, partially visible objects, and a broad spectrum of buildings. We propose a new \emph{projection pooling layer}, creating a unified, top-view representation of the top-view and the side views in a high-dimensional space. It allows us to utilize the building and imagery metadata seamlessly. Introducing this layer improves classification accuracy -- compared to highly tuned baseline models -- indicating its suitability for building analysis

The devil is in the decoder

Many machine vision applications require predictions for every pixel of the input image (for example semantic segmentation, boundary detection). Models for such problems usually consist of encoders which decreases spatial resolution while learning a high-dimensional representation, followed by decoders who recover the original input resolution and result in low-dimensional predictions. While encoders have been studied rigorously, relatively few studies address the decoder side. Therefore this paper presents an extensive comparison of a variety of decoders for a variety of pixel-wise prediction tasks. Our contributions are: (1) Decoders matter: we observe significant variance in results between different types of decoders on various problems. (2) We introduce a novel decoder: bilinear additive upsampling. (3) We introduce new residual-like connections for decoders. (4) We identify two decoder types which give a consistently high performance

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation

Spectroscopic issues in optical polarization of

The Magnetic Resonance Imaging (MRI) of human lungs for diagnostic purposes became possible by using nuclear spin hyperpolarized noble gases, such as 3He. One of the methods to polarize 3He is the Metastability Exchange Optical Pumping (MEOP), which up to now has been performed at low pressure of about 1 mbar and in low magnetic field below 0.1 T (standard conditions). The equilibrium nuclear polarization can reach up to 80%, but it is dramatically reduced during the subsequent gas compression to the atmospheric pressure that is necessary for the lungs examination. Further polarization losses occur during the transportation of the gas to the hospital scanner. It was shown recently that up to 50% polarization can be obtained at elevated pressure exceeding 20 mbar, by using magnetic field higher than 0.1 T (nonstandard conditions). Therefore, following the construction of the low-field MEOP polarizer located in the lab, a dedicated portable unit was developed, which uses the magnetic field of the 1.5 T MR medical scanner and works in the continuous-flow regime. The first in Poland MRI images of human lungs in vivo were obtained on the upgraded to 3He resonance frequency Siemens Sonata medical scanner. An evident improvement in the image quality was achieved when using the new technique. The paper shows how spectroscopic measurements of 3He carried out in various experimental conditions led both to useful practical results and to significant progress in understanding fundamental processes taking place during MEOP

K Nearest Neighbor Classification with Local Induction of the Simple Value Difference Metric

The classical k nearest neighbor (k-nn) classification assumes that a fixed global metric is defined and searching for nearest neighbors is always based on this global metric. In the paper we present a model with local induction of a metric. Any test object induces a local metric from the neighborhood of this object and selects k nearest neighbors according to this locally induced metric. To induce both the global and the local metric we use the weighted Simple Value Difference Metric (SVDM). The experimental results show that the proposed classification model with local induction of a metric reduces classification error up to several times in comparison to the classical k-nn method

A high-field ^{3}He metastability exchange optical pumping polarizer operating in a 1.5 T medical scanner for lung magnetic resonance imaging

After being hyperpolarized using the technique of Metastability Exchange Optical Pumping (MEOP), 3He can be used as a contrast agent for lung magnetic resonance imaging (MRI). MEOP is usually performed at low magnetic field (~ 1 mT) and low pressure (~ 1 mbar), which results in a low magnetization production rate. A delicate polarization-preserving step of compression is also required. It was demonstrated in sealed cells that high nuclear polarization values can be obtained at higher pressures with MEOP, if performed at high magnetic field (non-standard conditions). In this work the feasibility of building a high-field polarizer that operates within a commercial 1.5 T scanner was evaluated. Preliminary measurements of nuclear polarization with sealed cells filled at different 3He gas pressures (1.33 to 267 mbar) were performed. The use of an annular shape for the laser beam increased by 25 % the achievable nuclear polarization equilibrium value (Meq) at 32 and 67 mbar as compared to a Gaussian beam shape. Meq values of 66.4 and 31 % were obtained at 32 and 267 mbar respectively and the magnetization production rate was increased by a factor of 10 compared to the best results obtained under standard conditions. To study the reproducibility of the method in a polarizing system, the same experiments were performed with small cells connected to a gas handling system. The polarization build-up times were approximately 3 times longer in the 20-30 mbar range of pressure than those obtained for the 32 mbar sealed cell. However, reasonable Meq values of 40-60 % were achieved in a 90 mL open cell. Based on these findings, a novel compact polarizing system was designed and built. Its typical output is a 3He gas flow rate of 15 sccm with a polarization of 33 %. In-vivo lung MRI ventilation images were acquired to demonstrate the polarizer s application

The rough set exploration system

This article gives an overview of the Rough Set Exploration System (RSES). RSES is a freely available software system toolset for data exploration, classification support and knowledge discovery. The main functionalities of this software system are presented along with a brief explanation of the algorithmic methods used by RSES. Many of the RSES methods have originated from rough set theory introduced by Zdzislaw Pawlak during the early 1980s