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Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is in- formative and important to understand motion mechanisms of body regions. Modeling such in- formation into the MRI reconstruction process produces temporally coherent image sequence and reduces imaging artifacts and blurring. However, existing deep learning based approaches neglect motion information during the reconstruction procedure, while traditional motion-guided methods are hindered by heuristic parameter tuning and long inference time. We propose a novel dynamic MRI reconstruction approach called MODRN that unitizes deep neural networks with motion in- formation to improve reconstruction quality. The central idea is to decomposemore »
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Existing neural cell tracking methods generally use the morphology cell features for data association. However, these features are limited to the quality of cell segmentation and are prone to errors for mitosis determination. To over- come these issues, in this work we propose an online multi- object tracking method that leverages both cell appearance and motion features for data association. In particular, we propose a supervised blob-seed network (BSNet) to predict the cell appearance features and an unsupervised optical flow network (UnFlowNet) for capturing the cell motions. The data association is then solved using the Hungarian al- gorithm. Experimental evaluationmore »
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Nuclei segmentation is a fundamental task in histopathological image analysis. Typically, such segmentation tasks require significant effort to manually generate pixel-wise annotations for fully supervised training. To alleviate the manual effort, in this paper we propose a novel approach using points only annotation. Two types of coarse labels with complementary information are derived from the points annotation, and are then utilized to train a deep neural network. The fully- connected conditional random field loss is utilized to further refine the model without introducing extra computational complexity during inference. Experimental results on two nuclei segmentation datasets reveal that the proposed methodmore »
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We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can prac- tically benefit patient due to reduced time of MRI scan, but it is also challenging since quality of reconstruction may be compromised. Currently, deep learning based methods dom- inate MRI reconstruction over traditional approaches such as Compressed Sensing, but they rarely show satisfactory performance in the case of low undersampled k-space data. One explanation is that these methods treat channel-wise fea- tures equally, which results in degraded representation ability of the neural network. To solve this problem, we propose amore »
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Neural cell instance segmentation serves as a valuable tool for the study of neural cell behaviors. In general, the instance segmentation methods compute the region of interest (ROI) through a detection module, where the segmentation is sub- sequently performed. To precisely segment the neural cells, especially their tiny and slender structures, existing work em- ploys a u-net structure to preserve the low-level details and encode the high-level semantics. However, such method is insufficient for differentiating the adjacent cells when large parts of them are included in the same cropped ROI. To solve this problem, we propose a context-refined neural cellmore »
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Nuclei segmentation and classification are two important tasks in the histopathology image analysis, because the mor- phological features of nuclei and spatial distributions of dif- ferent types of nuclei are highly related to cancer diagnosis and prognosis. Existing methods handle the two problems independently, which are not able to obtain the features and spatial heterogeneity of different types of nuclei at the same time. In this paper, we propose a novel deep learning based method which solves both tasks in a unified framework. It can segment individual nuclei and classify them into tumor, lymphocyte and stroma nuclei. Perceptual loss ismore »
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A bstract A search for a heavy resonance decaying into a top quark and a W boson in proton-proton collisions at $$ \sqrt{s} $$ s = 13 TeV is presented. The data analyzed were recorded with the CMS detector at the LHC and correspond to an integrated luminosity of 138 fb − 1 . The top quark is reconstructed as a single jet and the W boson, from its decay into an electron or muon and the corresponding neutrino. A top quark tagging technique based on jet clustering with a variable distance parameter and simultaneous jet grooming is used tomore »Free, publicly-accessible full text available April 1, 2023
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Free, publicly-accessible full text available March 1, 2023
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