3,153 research outputs found
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Model architectures have been dramatically increasing in size, improving
performance at the cost of resource requirements. In this paper we propose 3DQ,
a ternary quantization method, applied for the first time to 3D Fully
Convolutional Neural Networks (F-CNNs), enabling 16x model compression while
maintaining performance on par with full precision models. We extensively
evaluate 3DQ on two datasets for the challenging task of whole brain
segmentation. Additionally, we showcase our method's ability to generalize on
two common 3D architectures, namely 3D U-Net and V-Net. Outperforming a variety
of baselines, the proposed method is capable of compressing large 3D models to
a few MBytes, alleviating the storage needs in space critical applications.Comment: Accepted to MICCAI 201
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans
The magnetic resonance (MR) analysis of brain tumors is widely used for
diagnosis and examination of tumor subregions. The overlapping area among the
intensity distribution of healthy, enhancing, non-enhancing, and edema regions
makes the automatic segmentation a challenging task. Here, we show that a
convolutional neural network trained on high-contrast images can transform the
intensity distribution of brain lesions in its internal subregions.
Specifically, a generative adversarial network (GAN) is extended to synthesize
high-contrast images. A comparison of these synthetic images and real images of
brain tumor tissue in MR scans showed significant segmentation improvement and
decreased the number of real channels for segmentation. The synthetic images
are used as a substitute for real channels and can bypass real modalities in
the multimodal brain tumor segmentation framework. Segmentation results on
BraTS 2019 dataset demonstrate that our proposed approach can efficiently
segment the tumor areas. In the end, we predict patient survival time based on
volumetric features of the tumor subregions as well as the age of each case
through several regression models
Increased collagen synthesis rate during wound healing in muscle
Wound healing in muscle involves the deposition of collagen, but it is not known whether this is achieved by changes in the synthesis or the degradation of collagen. We have used a reliable flooding dose method to measure collagen synthesis rate in vivo in rat abdominal muscle following a surgical incision. Collagen synthesis rate was increased by 480% and 860% on days 2 and 7 respectively after surgery in the wounded muscle compared with an undamaged area of the same muscle. Collagen content was increased by approximately 100% at both day 2 and day 7. These results demonstrate that collagen deposition during wound healing in muscle is achieved entirely by an increase in the rate of collagen synthesis
Cascaded two-photon nonlinearity in a one-dimensional waveguide with multiple two-level emitters
We propose and theoretically investigate a model to realize cascaded optical
nonlinearity with few atoms and photons in one-dimension (1D). The optical
nonlinearity in our system is mediated by resonant interactions of photons with
two-level emitters, such as atoms or quantum dots in a 1D photonic waveguide.
Multi-photon transmission in the waveguide is nonreciprocal when the emitters
have different transition energies. Our theory provides a clear physical
understanding of the origin of nonreciprocity in the presence of cascaded
nonlinearity. We show how various two-photon nonlinear effects including
spatial attraction and repulsion between photons, background fluorescence can
be tuned by changing the number of emitters and the coupling between emitters
(controlled by the separation).Comment: 6 pages, 4 figure
Loneliness, social support and cardiovascular reactivity to laboratory stress
Self-reported or explicit loneliness and social support have been inconsistently associated with cardiovascular reactivity (CVR) to stress. The present study aimed to adapt an implicit measure of loneliness, and use it alongside the measures of explicit loneliness and social support, to investigate their correlations with CVR to laboratory stress. Twenty-five female volunteers aged between 18 and 39 years completed self-reported measures of loneliness and social support, and an Implicit Association Test (IAT) of loneliness. The systolic blood pressure (SBP), diastolic blood pressure (DBP) and heart rate (HR) reactivity indices were measured in response to psychosocial stress induced in the laboratory. Functional support indices of social support were significantly correlated with CVR reactivity to stress. Interestingly, implicit, but not explicit, loneliness was significantly correlated with DBP reactivity after one of the stressors. No associations were found between structural support and CVR indices. Results are discussed in terms of validity of implicit versus explicit measures and possible factors that affect physiological outcomes
Learning Visual Context by Comparison
Finding diseases from an X-ray image is an important yet highly challenging
task. Current methods for solving this task exploit various characteristics of
the chest X-ray image, but one of the most important characteristics is still
missing: the necessity of comparison between related regions in an image. In
this paper, we present Attend-and-Compare Module (ACM) for capturing the
difference between an object of interest and its corresponding context. We show
that explicit difference modeling can be very helpful in tasks that require
direct comparison between locations from afar. This module can be plugged into
existing deep learning models. For evaluation, we apply our module to three
chest X-ray recognition tasks and COCO object detection & segmentation tasks
and observe consistent improvements across tasks. The code is available at
https://github.com/mk-minchul/attend-and-compare.Comment: ECCV 2020 spotlight pape
Covalently interconnected transition metal dichalcogenide networks via defect engineering for high-performance electronic devices.
Solution-processed semiconducting transition metal dichalcogenides are at the centre of an ever-increasing research effort in printed (opto)electronics. However, device performance is limited by structural defects resulting from the exfoliation process and poor inter-flake electronic connectivity. Here, we report a new molecular strategy to boost the electrical performance of transition metal dichalcogenide-based devices via the use of dithiolated conjugated molecules, to simultaneously heal sulfur vacancies in solution-processed transition metal disulfides and covalently bridge adjacent flakes, thereby promoting percolation pathways for the charge transport. We achieve a reproducible increase by one order of magnitude in field-effect mobility (µFE), current ratio (ION/IOFF) and switching time (τS) for liquid-gated transistors, reaching 10-2 cm2 V-1 s-1, 104 and 18 ms, respectively. Our functionalization strategy is a universal route to simultaneously enhance the electronic connectivity in transition metal disulfide networks and tailor on demand their physicochemical properties according to the envisioned applications.European Commission through the Graphene Flagship, the ERC Grants SUPRA2DMAT (GA-833707), FUTURE-PRINT (GA-694101), Hetero2D, GSYNCOR, the EU Grant Neurofibres, the Agence Nationale de la Recherche through the Labex projects CSC (ANR-10-LABX-0026 CSC) and NIE (ANR-11-LABX-0058 NIE) within the Investissement d’Avenir program (ANR-10-120 IDEX-0002-02), the International Center for Frontier Research in Chemistry (icFRC), EPSRC Grants EP/K01711X/1, EP/K017144/1, EP/N010345/1, EP/L016057/1, and the Faraday Institution. The HAADF-STEM characterization was carried out in the Advanced Microscopy Laboratory (Dublin), a Science Foundation Ireland (SFI) supported centre
Bio-inspired Attentive Segmentation of Retinal OCT Imaging
Albeit optical coherence imaging (OCT) is widely used to assess ophthalmic pathologies, localization of intra-retinal boundaries suffers from erroneous segmentations due to image artifacts or topological abnormalities. Although deep learning-based methods have been effectively applied in OCT imaging, accurate automated layer segmentation remains a challenging task, with the flexibility and precision of most methods being highly constrained. In this paper, we propose a novel method to segment all retinal layers, tailored to the bio-topological OCT geometry. In addition to traditional learning of shift-invariant features, our method learns in selected pixels horizontally and vertically, exploiting the orientation of the extracted features. In this way, the most discriminative retinal features are generated in a robust manner, while long-range pixel dependencies across spatial locations are efficiently captured. To validate the effectiveness and generalisation of our method, we implement three sets of networks based on different backbone models. Results on three independent studies show that our methodology consistently produces more accurate segmentations than state-of-the-art networks, and shows better precision and agreement with ground truth. Thus, our method not only improves segmentation, but also enhances the statistical power of clinical trials with layer thickness change outcomes
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