Results 61 to 70 of about 36,711 (232)

Crossed Dual-Branch U-Net for Hyperspectral Image Super-Resolution

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral images have gained great achievements in many fields, but their low spatial resolution limits the effectiveness in applications.
Jingyi Zhang, Jianjun Liu, Jinlong Yang, Zebin Wu   +3 more
doaj   +1 more source

Discovery and Targeted Proteomic Studies Reveal Striatal Markers Validated for Huntington's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Clinical trials for Huntington's disease (HD) enrolling persons before clinical motor diagnosis (CMD) lack validated biomarkers. This study aimed to conduct an unbiased discovery analysis and a targeted examination of proteomic biomarkers scrutinized by clinical validation. Methods Cerebrospinal fluid was obtained from PREDICT‐HD and
Daniel Chelsky, Cara Joyce, H. Jeremy Bockholt, Paul A. Rudnick, William H. Adams, Fiona McAllister, Justin W. Smock, Michael A. Newton, Jane S. Paulsen   +8 more
wiley   +1 more source

Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification

Few-shot image classification remains a critical challenge in the field of computer vision, particularly in data-scarce environments. Existing methods typically rely on pre-trained visual-language models, such as CLIP. However, due to the modality gap, which is the inconsistent distribution of image and text features in the joint embedding space ...
Yang, Xi, Peng, Pai, Xie, Wulin, Lu, Xiaohuan, Wen, Jie   +4 more
openaire   +2 more sources

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu, Qi Wang, Suhui Kuang, Dezhi Cao, Ping Ding, Shaohui Zhang, Haihua Wei, Zhirong Wei, Jinshan Xu, Xinyu Huang, Bing Liu, Shuli Liang   +11 more
wiley   +1 more source

Mutually Orthogonal Softmax Axes for Cross-Domain Retrieval

IEEE Access, 2020
In this paper, we introduce and address a crucial but less accentuated problem in cross-domain retrieval task. We first highlight the challenge caused by diversity of inter-class similarities across different domains.
Hae-Chan Noh, Jae-Pil Heo
doaj   +1 more source

Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP

Contrastive Language--Image Pre-training (CLIP) has manifested remarkable improvements in zero-shot classification and cross-modal vision-language tasks. Yet, from a geometrical point of view, the CLIP embedding space has been found to have a pronounced modality gap.
Eslami, Sedigheh, de Melo, Gerard
openaire   +2 more sources

Bridging the Gap: Multi-Level Cross-Modality Joint Alignment for Visible-Infrared Person Re-Identification

IEEE Transactions on Circuits and Systems for Video Technology
10 pages, 4 figures, 5 ...
Tengfei Liang, Yi Jin, Wu Liu, Tao Wang, Songhe Feng, Yidong Li   +5 more
openaire   +2 more sources

Development of a Prediction Model for Progression Risk in High‐Grade Gliomas Based on Habitat Radiomics and Pathomics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu, Yuxi Gong, Weilin Xu, Xingjian Sun, Gefei Jiang, Lei Qiu, Kexin Shi, Mengxing Wu, Yinjiao Fei, Jinling Yuan, Jinyan Luo, Yurong Li, Yuandong Cao, Minhong Pan, Shu Zhou   +14 more
wiley   +1 more source

Hierarchical Fusion Transformer for Multimodal Ground-Based Cloud Type Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Existing methods for multimodal ground-based cloud type classification are dominated by convolutional neural networks, and it fails to capture long-range dependencies.
Shuang Liu, Zeyu Yu, Zhong Zhang, Chaojun Shi, Baihua Xiao   +4 more
doaj   +1 more source

Clinically Relevant Outcome Measures in Women With Adrenoleukodystrophy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Adrenoleukodystrophy is a rare inherited peroxisomal disease caused by pathogenic variants in the ABCD1 gene located on the X chromosome. Although the most severe central nervous system and adrenal complications typically affect only men with adrenoleukodystrophy, the majority of women develop myeloneuropathy symptoms in adulthood.
Chenwei Yan, Elizabeth I. Pierpont, Amena S. Fine, Reena V. Kartha   +3 more
wiley   +1 more source