424 research outputs found
Supplemental Material - Identification of monocyte-associated biomarkers in systemic lupus erythematosus and their pan-cancer analysis
No full textSupplemental Material for Identification of monocyte-associated biomarkers in systemic lupus erythematosus and their pan-cancer analysis by Huiting Chen, Jinxuan He, Linwei Wang, Yanbin Lin, Zhixiang Mou, Xiaoxuan Huang, and Lan Chen in Lupus.</p
Frequency-Adaptive Dilated Convolution for Semantic Segmentation
No full textDilated convolution, which expands the receptive field by inserting gaps
between its consecutive elements, is widely employed in computer vision. In
this study, we propose three strategies to improve individual phases of dilated
convolution from the view of spectrum analysis. Departing from the conventional
practice of fixing a global dilation rate as a hyperparameter, we introduce
Frequency-Adaptive Dilated Convolution (FADC), which dynamically adjusts
dilation rates spatially based on local frequency components. Subsequently, we
design two plug-in modules to directly enhance effective bandwidth and
receptive field size. The Adaptive Kernel (AdaKern) module decomposes
convolution weights into low-frequency and high-frequency components,
dynamically adjusting the ratio between these components on a per-channel
basis. By increasing the high-frequency part of convolution weights, AdaKern
captures more high-frequency components, thereby improving effective bandwidth.
The Frequency Selection (FreqSelect) module optimally balances high- and
low-frequency components in feature representations through spatially variant
reweighting. It suppresses high frequencies in the background to encourage FADC
to learn a larger dilation, thereby increasing the receptive field for an
expanded scope. Extensive experiments on segmentation and object detection
consistently validate the efficacy of our approach. The code is publicly
available at https://github.com/Linwei-Chen/FADC.Comment: CVPR 2024 highlighte
sj-docx-1-chl-10.1177_17475198231180835 – Supplemental material for Synthesis and anti-leukemia activity of phorbol 13,20-diesters and phorbol 12,13,20-triesters
No full textSupplemental material, sj-docx-1-chl-10.1177_17475198231180835 for Synthesis and anti-leukemia activity of phorbol 13,20-diesters and phorbol 12,13,20-triesters by Yan Wang, Yu Shan, Rui Feng, Siyu Wang, Linwei Li, Shu Xu, Yu Chen, Xu Feng, Jinyue Luo and Fei Liu in Journal of Chemical Research</p
Stochastic optimization with decisions truncated by random variables and its applications in operations
No full textWe study stochastic optimization problems with decisions truncated by random variables and its applications in operations management. The technical difficulty of these problems is that the optimization problem is not convex due to the truncation. We develop a transformation technique to convert the original non-convex optimization problems to convex ones while preservation some desired structural properties, which are useful for characterizing optimal decision policies and conducting comparative statics. Our transformation technique provides a unified approach to analyze a broad class of models in inventory control and revenue management. In additional, we develop efficient algorithms to solve the transformed stochastic optimization problem.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-08-01The student, Xiangyu Gao, accepted the attached license on 2017-06-23 at 09:21.The student, Xiangyu Gao, submitted this Dissertation for approval on 2017-06-23 at 09:23.This Dissertation was approved for publication on 2017-06-26 at 13:18.DSpace SAF Submission Ingestion Package generated from Vireo submission #11220 on 2017-09-29 at 11:13:44Made available in DSpace on 2017-09-29T16:39:09Z (GMT). No. of bitstreams: 3
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Frequency-aware Feature Fusion for Dense Image Prediction
No full textDense image prediction tasks demand features with strong category information and precise spatial boundary details at high resolution. To achieve this, modern hierarchical models often utilize feature fusion, directly adding upsampled coarse features from deep layers and high-resolution features from lower levels. In this paper, we observe rapid variations in fused feature values within objects, resulting in intra-category inconsistency due to disturbed high-frequency features. Additionally, blurred boundaries in fused features lack accurate high frequency, leading to boundary displacement. Building upon these observations, we propose Frequency-Aware Feature Fusion (FreqFusion), integrating an Adaptive Low-Pass Filter (ALPF) generator, an offset generator, and an Adaptive High-Pass Filter (AHPF) generator. The ALPF generator predicts spatially-variant low-pass filters to attenuate high-frequency components within objects, reducing intra-class inconsistency during upsampling. The offset generator refines large inconsistent features and thin boundaries by replacing inconsistent features with more consistent ones through resampling, while the AHPF generator enhances high-frequency detailed boundary information lost during downsampling. Comprehensive visualization and quantitative analysis demonstrate that FreqFusion effectively improves feature consistency and sharpens object boundaries. Extensive experiments across various dense prediction tasks confirm its effectiveness. The code is made publicly available at https://github.com/Linwei-Chen/FreqFusion.Accepted by TPAMI (2024
DisQ: Disentangling Quantitative MRI Mapping of the Heart
No full textQuantitative MRI (qMRI) of the heart has become an important clinical tool for examining myocardial tissue properties. Because heart is a moving object, it is usually imaged with electrocardiogram and respiratory gating during acquisition, to “freeze” its motion. In reality, gating is more-often-than-not imperfect given the heart rate variability and nonideal breath-hold. qMRI of the heart, consequently, is characteristic of varying image contrast as well as residual motion, the latter compromising the quality of quantitative mapping. Motion correction is an important step prior to parametric mapping, however, a long-standing difficulty for registering the dynamic sequence is that the contrast across frames varies wildly: depending on the acquisition scheme some frames can have extremely poor contrast, which fails both traditional optimization-based and modern learning-based registration methods. In this work, we propose a novel framework named DisQ, which Disentangles Quantitative mapping sequences into the latent space of contrast and anatomy, fully unsupervised. The disentangled latent spaces serve for the purpose of generating a series of images with identical contrast, which enables easy and accurate registration of all frames. We applied our DisQ method to the modified Look-Locker inversion recovery (MOLLI) sequence, and demonstrated improved performance of T1 mapping. In addition, we showed the possibility of generating a dynamic series of baseline images with exactly the same shape, strictly registered and perfectly “frozen". Our proposed DisQ methodology readily extends to other types of cardiac qMRI such as T2 mapping and perfusion.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ImPhys/Medical Imagin
Electrocardiographic Imaging
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Electrocardiographic Imaging
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Efficient Bayesian Uncertainty Estimation for nnU-Net
No full textThe self-configuring nnU-Net has achieved leading performance in a large
range of medical image segmentation challenges. It is widely considered as the
model of choice and a strong baseline for medical image segmentation. However,
despite its extraordinary performance, nnU-Net does not supply a measure of
uncertainty to indicate its possible failure. This can be problematic for
large-scale image segmentation applications, where data are heterogeneous and
nnU-Net may fail without notice. In this work, we introduce a novel method to
estimate nnU-Net uncertainty for medical image segmentation. We propose a
highly effective scheme for posterior sampling of weight space for Bayesian
uncertainty estimation. Different from previous baseline methods such as Monte
Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does
not require a variational architecture and keeps the original nnU-Net
architecture intact, thereby preserving its excellent performance and ease of
use. Additionally, we boost the segmentation performance over the original
nnU-Net via marginalizing multi-modal posterior models. We applied our method
on the public ACDC and M&M datasets of cardiac MRI and demonstrated improved
uncertainty estimation over a range of baseline methods. The proposed method
further strengthens nnU-Net for medical image segmentation in terms of both
segmentation accuracy and quality control
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