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

General Messenger Gauge Mediation

We discuss theories of gauge mediation in which the hidden sector consists of two subsectors which are weakly coupled to each other. One sector is made up of messengers and the other breaks supersymmetry. Each sector by itself may be strongly coupled. We provide a unifying framework for such theories and discuss their predictions in different settings. We show how this framework incorporates all known models of messengers. In the case of weakly-coupled messengers interacting with spurions through the superpotential, we prove that the sfermion mass-squared is positive, and furthermore, that there is a lower bound on the ratio of the sfermion mass to the gaugino mass.Comment: 37 pages; minor change

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