Adversarial Inpainting of Medical Image Modalities

Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of medical images via Generative Adversarial Networks (GANs). The proposed framework incorporates two patch-based discriminator networks with additional style and perceptual losses for the inpainting of missing information in realistically detailed and contextually consistent manner. The proposed framework outperformed other natural image inpainting techniques both qualitatively and quantitatively on two different medical modalities.Comment: To be submitted to ICASSP 201

An Adversarial Super-Resolution Remedy for Radar Design Trade-offs

Radar is of vital importance in many fields, such as autonomous driving, safety and surveillance applications. However, it suffers from stringent constraints on its design parametrization leading to multiple trade-offs. For example, the bandwidth in FMCW radars is inversely proportional with both the maximum unambiguous range and range resolution. In this work, we introduce a new method for circumventing radar design trade-offs. We propose the use of recent advances in computer vision, more specifically generative adversarial networks (GANs), to enhance low-resolution radar acquisitions into higher resolution counterparts while maintaining the advantages of the low-resolution parametrization. The capability of the proposed method was evaluated on the velocity resolution and range-azimuth trade-offs in micro-Doppler signatures and FMCW uniform linear array (ULA) radars, respectively.Comment: Accepted in EUSIPCO 2019, 5 page

From Nothing – Mimetic Seeing and Making

Mimesis has held a central role in art making since ancient times as a primary means of apprehending the real. This exegesis is an explication of the various mimetic functions that have endured in modern and contemporary art practises such as the readymade and its sculptural simulation. I consider key works by Andy Warhol, Marcel Duchamp, John Cage and Peter Fischli and David Weiss, examining their relationship to the concept and practise of mimesis. I address mimetic representation and replication in dialogue with philosophers such as Arthur Danto, Gorgio Agamben, Jean Baudrillard, Maurice Blanchot and Jennifer Anna Gosetti-Ferencei. This research asks if there is a fundamental difference between the mimetic activity in ancient Greek statuary and in modern and contemporary art practise through an exploration of the connections between trompe l’oeil and the quotidian as reciprocal and interdependent. Additionally, the exegesis seeks to clarify the complex relationship between formal philosophical thought and the operations of creative labour, highlighting points of intersection and divergence between two distinct modes of ‘thinking’. This distinction positions philosophical thought within mentally constructed concepts and artistic thought within mentally constructed images. The discussion provides a setting for my studio practise and brings into question the process of mimetic replication as a necessary additional step in the production of my work following the initial creation of a sculptural assemblage. In both activities—the configuration of found objects and the casting of their copies—I have discovered that the sculpture’s conceptual effect is echoed in the physical enactment of its fabrication. This exegesis is therefore an elucidation of my studio process, where the concerted acts of seeing, configuring and manufacturing retrieve representational sculptural objects from nothingness

On semi-planar Steiner quasigroups

AbstractA Steiner triple system (briefly ST) is in 1–1 correspondence with a Steiner quasigroup or squag (briefly SQ) [B. Ganter, H. Werner, Co-ordinatizing Steiner systems, Ann. Discrete Math. 7 (1980) 3–24; C.C. Lindner, A. Rosa, Steiner quadruple systems: A survey, Discrete Math. 21 (1979) 147–181]. It is well known that for each n≡1 or 3 (mod 6) there is a planar squag of cardinality n [J. Doyen, Sur la structure de certains systems triples de Steiner, Math. Z. 111 (1969) 289–300]. Quackenbush expected that there should also be semi-planar squags [R.W. Quackenbush, Varieties of Steiner loops and Steiner quasigroups, Canad. J. Math. 28 (1976) 1187–1198]. A simple squag is semi-planar if every triangle either generates the whole squag or the 9-element squag. The first author has constructed a semi-planar squag of cardinality 3n for all n>3 and n≡1 or 3 (mod 6) [M.H. Armanious, Semi-planar Steiner quasigroups of cardinality 3n, Australas. J. Combin. 27 (2003) 13–27]. In fact, this construction supplies us with semi-planar squags having only nontrivial subsquags of cardinality 9. Our aim in this article is to give a recursive construction as n→3n for semi-planar squags. This construction permits us to construct semi-planar squags having nontrivial subsquags of cardinality >9. Consequently, we may say that there are semi-planar SQ(3mn)s (or semi-planar ST(3mn)s) for each positive integer m and each n≡1 or 3 (mod 6) with n>3 having only medial subsquags at most of cardinality 3ν (sub-ST(3)ν) for each ν∈{1,2,…,m+1}

Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs

Motion artifacts are a primary source of magnetic resonance (MR) image quality deterioration with strong repercussions on diagnostic performance. Currently, MR motion correction is carried out either prospectively, with the help of motion tracking systems, or retrospectively by mainly utilizing computationally expensive iterative algorithms. In this paper, we utilize a new adversarial framework, titled MedGAN, for the joint retrospective correction of rigid and non-rigid motion artifacts in different body regions and without the need for a reference image. MedGAN utilizes a unique combination of non-adversarial losses and a new generator architecture to capture the textures and fine-detailed structures of the desired artifact-free MR images. Quantitative and qualitative comparisons with other adversarial techniques have illustrated the proposed model performance.Comment: 5 pages, 2 figures, under review for the IEEE International Symposium for Biomedical Image

Environmental Impact Assessment for Projects in the Nile Basin Countries

Environmental Impact Assessment (EIA) is a key aspect of many large-scale planning applications. It is a technique which is meant to help in understanding the potential environmental impacts of major development proposals. Unfortunately, the process and the outcome of EIA can be complex and confusing, leaving local communities unsure as to how a development might affect them. The objective of this research is to provide a strategic environmental framework for the environmentally sustainable development of the Nile River Basin, to improve the understanding of the relationship between water resources development and environmental conservation in the Basin, and to provide a forum to discuss development paths for the Nile with a wide range of stakeholders. Focusing on transboundary issues provides the riparian countries with a major opportunity to make significant progress towards their economic and environmental goals in ways that have proved difficult to achieve independently. In addition, the paper analyzes some EIAs carried into Egypt, which share the Nile as a common environmental resource with the other Nile basin countries, and discusses how improvements of guidelines and unification of legislation can improve cooperation among these countries. Finally, the paper recommends an EIA process revision to be implemented for effective EIA practice in the Nile Basin Countries

MedGAN: Medical Image Translation using GANs

Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.Comment: 16 pages, 8 figure

Determination of Nano-sized Adsorbate Mass in Solution using Mechanical Resonators: Elimination of the so far Inseparable Liquid Contribution

Assumption-free mass quantification of nanofilms, nanoparticles, and (supra)molecular adsorbates in liquid environment remains a key challenge in many branches of science. Mechanical resonators can uniquely determine the mass of essentially any adsorbate; yet, when operating in liquid environment, the liquid dynamically coupled to the adsorbate contributes significantly to the measured response, which complicates data interpretation and impairs quantitative adsorbate mass determination. Employing the Navier-Stokes equation for liquid velocity in contact with an oscillating surface, we show that the liquid contribution can be eliminated by measuring the response in solutions with identical kinematic viscosity but different densities. Guided by this insight, we used quartz crystal microbalance (QCM), one of the most widely-employed mechanical resonator, to demonstrate that kinematic-viscosity matching can be utilized to accurately quantify the dry mass of systems such as adsorbed rigid nanoparticles, tethered biological nanoparticles (lipid vesicles), as well as highly hydrated polymeric films. The same approach applied to the simultaneously measured energy dissipation made it possible to quantify the mechanical properties of the adsorbate and its attachment to the surface, as demonstrated by, for example, probing the hydrodynamic stablization induced by nanoparticles crowding. Finally, we envision that the possibility to simultaneously determine the dry mass and mechanical properties of adsorbates as well as the liquid contributions will provide the experimental tools to use mechanical resonators for applications beyond mass determination, as for example to directly interrogate the orientation, spatial distribution, and binding strength of adsorbates without the need for complementary techniques.Comment: 22 pages, 7 figure

AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks

Automatic speech recognition (ASR) systems are of vital importance nowadays in commonplace tasks such as speech-to-text processing and language translation. This created the need for an ASR system that can operate in realistic crowded environments. Thus, speech enhancement is a valuable building block in ASR systems and other applications such as hearing aids, smartphones and teleconferencing systems. In this paper, a generative adversarial network (GAN) based framework is investigated for the task of speech enhancement, more specifically speech denoising of audio tracks. A new architecture based on CasNet generator and an additional feature-based loss are incorporated to get realistically denoised speech phonetics. Finally, the proposed framework is shown to outperform other learning and traditional model-based speech enhancement approaches.Comment: 5 pages, 4 figures and 2 Tables. Accepted in EUSIPCO 202