260 research outputs found
Optically reconfigurable metadevices based on phase-change materials
Chalcogenide phase-change media provide a uniquely flexible platform for both nanostructured and optically-rewritable all-dielectric metamaterials. Non-volatile, laser-induced phase transitions enable resonance switching in nanostructured chalcogenide meta-surfaces and allow for reversible direct-writing of arbitrary meta-devices in chalcogenide thin films, including dynamically refocusable, chromatically correctable and super-oscillatory lenses, and near-infrared-resonant photonic metamaterials
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Positron emission tomography (PET) image synthesis plays an important role,
which can be used to boost the training data for computer aided diagnosis
systems. However, existing image synthesis methods have problems in
synthesizing the low resolution PET images. To address these limitations, we
propose multi-channel generative adversarial networks (M-GAN) based PET image
synthesis method. Different to the existing methods which rely on using
low-level features, the proposed M-GAN is capable to represent the features in
a high-level of semantic based on the adversarial learning concept. In
addition, M-GAN enables to take the input from the annotation (label) to
synthesize the high uptake regions e.g., tumors and from the computed
tomography (CT) images to constrain the appearance consistency and output the
synthetic PET images directly. Our results on 50 lung cancer PET-CT studies
indicate that our method was much closer to the real PET images when compared
with the existing methods.Comment: 9 pages, 2 figure
n-type chalcogenides by ion implantation.
Carrier-type reversal to enable the formation of semiconductor p-n junctions is a prerequisite for many electronic applications. Chalcogenide glasses are p-type semiconductors and their applications have been limited by the extraordinary difficulty in obtaining n-type conductivity. The ability to form chalcogenide glass p-n junctions could improve the performance of phase-change memory and thermoelectric devices and allow the direct electronic control of nonlinear optical devices. Previously, carrier-type reversal has been restricted to the GeCh (Ch=S, Se, Te) family of glasses, with very high Bi or Pb 'doping' concentrations (~5-11 at.%), incorporated during high-temperature glass melting. Here we report the first n-type doping of chalcogenide glasses by ion implantation of Bi into GeTe and GaLaSO amorphous films, demonstrating rectification and photocurrent in a Bi-implanted GaLaSO device. The electrical doping effect of Bi is observed at a 100 times lower concentration than for Bi melt-doped GeCh glasses.This work was supported by the UK EPSRC grants EP/I018417/1, EP/I019065/1 and EP/I018050/1.This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ncomms634
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Stannite quaternary cu2m(M = ni, co)sns4 as low cost inorganic hole transport materials in perovskite solar cells
In this study, inorganic stannite quaternary Cu2M(M = Ni, Co)SnS4 (CMTS) is explored as a low-cost, earth abundant, environmentally friendly and chemically stable hole transport material (HTM). CMTS nanoparticles were synthesized via a facile and mild solvothermal method and processed into aggregated nanoparticle inks, which were applied in n-i-p perovskite solar cells (PSCs). The results show that Cu2NiSnS4 (CNiTS) is more promising as an HTM than Cu2CoSnS4 (CCoTS), showing efficient charge injection as evidenced by considerable photoluminescence quenching and lower series resistance from Nyquist plots, as well as higher power conversion efficiency (PCE). Moreover, the perovskite layer coated by the CMTS HTM showed superior environmental stability after 200 h light soaking in 50% relative humidity, while organic HTMs suffer from a severe drop in perovskite absorption. Although the obtained PCEs are modest, this study shows that the cost effective and stable inorganic CMTSs are promising HTMs, which can contribute towards PSC commercialization, if the field can further optimize CMTS energy levels through compositional engineering.</jats:p
Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the
developing brain but is not suitable for anomaly screening. For this ultrasound
(US) is employed. While expert sonographers are adept at reading US images, MR
images are much easier for non-experts to interpret. Hence in this paper we
seek to produce images with MRI-like appearance directly from clinical US
images. Our own clinical motivation is to seek a way to communicate US findings
to patients or clinical professionals unfamiliar with US, but in medical image
analysis such a capability is potentially useful, for instance, for US-MRI
registration or fusion. Our model is self-supervised and end-to-end trainable.
Specifically, based on an assumption that the US and MRI data share a similar
anatomical latent space, we first utilise an extractor to determine shared
latent features, which are then used for data synthesis. Since paired data was
unavailable for our study (and rare in practice), we propose to enforce the
distributions to be similar instead of employing pixel-wise constraints, by
adversarial learning in both the image domain and latent space. Furthermore, we
propose an adversarial structural constraint to regularise the anatomical
structures between the two modalities during the synthesis. A cross-modal
attention scheme is proposed to leverage non-local spatial correlations. The
feasibility of the approach to produce realistic looking MR images is
demonstrated quantitatively and with a qualitative evaluation compared to real
fetal MR images.Comment: MICCAI-MLMI 201
Fetal brain tissue annotation and segmentation challenge results.
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero
Feature Selection via Chaotic Antlion Optimization
Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the
quality of the data training fitting) while minimizing the number of features used.This work was partially supported by the IPROCOM Marie Curie initial training network, funded
through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework
Programme FP7/2007-2013/ under REA grants agreement No. 316555, and by the Romanian
National Authority for Scientific Research, CNDIUEFISCDI, project number PN-II-PT-PCCA-2011-3.2-
0917. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript
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