MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma

Segmentation of nasopharyngeal carcinoma (NPC) from Magnetic Resonance Images (MRI) is a crucial prerequisite for NPC radiotherapy. However, manually segmenting of NPC is time-consuming and labor-intensive. Additionally, single-modality MRI generally cannot provide enough information for its accurate delineation. Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC. The backbone of MMFNet is designed as a multi-encoder-based network, consisting of several encoders to capture modality-specific features and one single decoder to fuse them and obtain high-level features for NPC segmentation. A fusion block is presented to effectively fuse features from multi-modality MRI. It firstly recalibrates low-level features captured from modality-specific encoders to highlight both informative features and regions of interest, then fuses weighted features by a residual fusion block to keep balance between fused ones and high-level features from decoder. Moreover, a training strategy named self-transfer, which utilizes pre-trained modality-specific encoders to initialize multi-encoder-based network, is proposed to make full mining of information from different modalities of MRI. The proposed method based on multi-modality MRI can effectively segment NPC and its advantages are validated by extensive experiments.Comment: 34 pages, 12 figure

Direct Counterfactual Communication with Single Photons

Intuition from our everyday lives gives rise to the belief that information exchanged between remote parties is carried by physical particles. Surprisingly, in a recent theoretical study [Salih H, Li ZH, Al-Amri M, Zubairy MS (2013) Phys Rev Lett 110:170502], quantum mechanics was found to allow for communication, even without the actual transmission of physical particles. From the viewpoint of communication, this mystery stems from a (nonintuitive) fundamental concept in quantum mechanics wave-particle duality. All particles can be described fully by wave functions. To determine whether light appears in a channel, one refers to the amplitude of its wave function. However, in counterfactual communication, information is carried by the phase part of the wave function. Using a single-photon source, we experimentally demonstrate the counterfactual communication and successfully transfer a monochrome bitmap from one location to another by using a nested version of the quantum Zeno effect.Comment: 13 pages, 3 figure

Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection

Depth cues with affluent spatial information have been proven beneficial in boosting salient object detection (SOD), while the depth quality directly affects the subsequent SOD performance. However, it is inevitable to obtain some low-quality depth cues due to limitations of its acquisition devices, which can inhibit the SOD performance. Besides, existing methods tend to combine RGB images and depth cues in a direct fusion or a simple fusion module, which makes they can not effectively exploit the complex correlations between the two sources. Moreover, few methods design an appropriate module to fully fuse multi-level features, resulting in cross-level feature interaction insufficient. To address these issues, we propose a novel Multi-level Cross-modal Interaction Network (MCINet) for RGB-D based SOD. Our MCI-Net includes two key components: 1) a cross-modal feature learning network, which is used to learn the high-level features for the RGB images and depth cues, effectively enabling the correlations between the two sources to be exploited; and 2) a multi-level interactive integration network, which integrates multi-level cross-modal features to boost the SOD performance. Extensive experiments on six benchmark datasets demonstrate the superiority of our MCI-Net over 14 state-of-the-art methods, and validate the effectiveness of different components in our MCI-Net. More important, our MCI-Net significantly improves the SOD performance as well as has a higher FPS

COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19

There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19. COVID-MTL learns different COVID-19 tasks in parallel through our novel random-weighted loss function, which assigns learning weights under Dirichlet distribution to prevent task dominance; our new 3D real-time augmentation algorithm (Shift3D) introduces space variances for 3D CNN components by shifting low-level feature representations of volumetric inputs in three dimensions; thereby, the MTL framework is able to accelerate convergence and improve joint learning performance compared to single-task models. By only using chest CT scans, COVID-MTL was trained on 930 CT scans and tested on separate 399 cases. COVID-MTL achieved AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, which outperformed the state-of-the-art models. Meanwhile, COVID-MTL yielded AUC of 0.800 $\pm$ 0.020 and 0.813 $\pm$ 0.021 (with transfer learning) for classifying control/suspected, mild/regular, and severe/critically-ill cases. To decipher the recognition mechanism, we also identified high-throughput lung features that were significantly related (P < 0.001) to the positivity and severity of COVID-19.Comment: COVID-19 research; computer vision and pattern recognition; 13 pages, 10 figures and 5 table

Extra-cavity-enhanced difference-frequency generation at 1.63 {\mu}m

A 1632-nm laser has highly important applications in interfacing the wavelength of rubidium-based quantum memories (795 nm) and the telecom band (typically 1550 nm) by frequency conversion in three-wave mixing processes. A 1632-nm laser source based on pump-enhanced difference frequency generation is demonstrated. It has 300 mW of output power, in agreement with simulations, and a 55% quantum efficiency. An average power fluctuation of 0.51% over one hour was observed, and 200-kHz linewidth was measured using a delayed self-heterodyne method.Comment: 7 pages, 5 figures, accepted by Journal of the Optical Society of America

Nonlinear frequency conversion and manipulation of vector beams in a Sagnac loop

Vector beams (VBs) are widely investigated for its special intensity and polarization distributions, which is useful for optical micromanipulation, optical micro-fabrication, optical communication, and single molecule imaging. To date, it is still a challenge to realize nonlinear frequency conversion (NFC) and manipulation of such VBs because of the polarization sensitivity in most of nonlinear processes. Here, we report an experimental realization of NFC and manipulation of VBs which can be used to expand the available frequency band. The main idea of our scheme is to introduce a Sagnac loop to solve the polarization dependence of NFC in nonlinear crystals. Furthermore, we find that a linearly polarized vector beam should be transformed to an exponential form before performing the NFC. The experimental results are well agree with our theoretical model. The present method is also applicable to other wave bands and second order nonlinear processes, and may also be generalized to the quantum regime for single photons.Comment: 8 pages, 3 figure

Revealing photons behaviors in a birefringent interferometer

The interferometer is one of the most important devices for revealing the nature of light and for precision optical metrology. Though lots of experiments were performed for probing photons behaviors in various configurations, a complete study of photons behavior in a birefringent interferometer has not ever been performed. Based on an environmental turbulence immune Mach-Zehnder interferometer, tunable photonic beatings by rotating a birefringent crystal versus the temperature of the crystal for both single-photon and two-photon are observed. Furthermore, the two-photon interference fringes beat two times faster than the single-photon interference fringes. This beating effect is used to determine the thermal dispersion coefficients of the two principal refractive axes with a single measurement, the two-photon interference shows super-resolution and high sensitivity. Obvious differences between two-photon and single photon interference are also revealed in an unbalanced situations. In addition, influences of the photon bandwidth to the beating behaviors that come from polarization-dependent decoherence are also investigated. Our findings will be important for better understanding the behavior of two-photon interference in a birefringent interferometer and for precision optical metrology with quantum enhancement.Comment: 13 pages, 5 figures. accepted in PR

Topological charge independent frequency conversion of twisted light

Light with orbital angular momentum (OAM), or twisted light, is widely investigated in the fields of optical communications, quantum information science and nonlinear optics by harnessing its unbounded dimension. For light-matter interacting with twisted light like quantum memory and nonlinear frequency conversion, efficiencies in these processes are usually decreasing exponentially with topological charges, which severely degrades the fidelity of the output states. Here we conceive and develop a method to eliminate the dependence of conversion efficiency on topological charges in second harmonic generation (SHG) process by utilizing a special designed image technique. The independence of SHG conversion efficiency on topological charge is verified for different topological charges, this independence is valid for various pump power. This method can be generalized to other light matter interaction processes and will revolute the field of light matter interaction with twisted light to achieve higher efficiency and higher fidelity.Comment: 5 pages, 3 figures, comments are welcom

Up-conversion imaging processing with field-of-view and edge enhancement

Spiral phase contrast is an important and convenient imaging processing technology in edge detection, and a broader field-of-view (FOV) of imaging is a long-pursuing aim to see more regions of the illumination objects. Compared with near-infrared (NIR) spectrum, the up-conversion imaging in visible spectrum benefits from the advantages of higher efficiency detection and lower potential speckle. FOV enhanced and spiral phase contrast up-conversion imaging processing methods by using second order nonlinear frequency up-conversion from NIR spectrum to visible spectrum in two different configurations are presented in this work. By changing the temperature of crystal, controllable spatial patterns of imaging with more than 4.5 times enhancement of FOV is realized in both configurations. Additionally, we present numerical simulations of the phenomenon, which agree well with the experimental observations. Our results provide a very promising way in imaging processing, which may be widely used in biomedicine, remote sensing and up-conversion monitoring.Comment: 11 pages,5 figures,22 reference

Contrast-weighted Dictionary Learning Based Saliency Detection for Remote Sensing Images

Object detection is an important task in remote sensing image analysis. To reduce the computational complexity of redundant information and improve the efficiency of image processing, visual saliency models have been widely applied in this field. In this paper, a novel saliency detection model based on Contrast-weighted Dictionary Learning (CDL) is proposed for remote sensing images. Specifically, the proposed CDL learns salient and non-salient atoms from positive and negative samples to construct a discriminant dictionary, in which a contrast-weighted term is proposed to encourage the contrast-weighted patterns to be present in the learned salient dictionary while discouraging them from being present in the non-salient dictionary. Then, we measure the saliency by combining the coefficients of the sparse representation (SR) and reconstruction errors. Furthermore, by using the proposed joint saliency measure, a variety of saliency maps are generated based on the discriminant dictionary. Finally, a fusion method based on global gradient optimization is proposed to integrate multiple saliency maps. Experimental results on four datasets demonstrate that the proposed model outperforms other state-of-the-art methods