Results 71 to 80 of about 4,718,784 (295)
CTNet: multi-modal channel attention transformer network for breast cancer image classification
Deep learning has significantly enhanced diagnostic accuracy in medical imaging, particularly in breast cancer detection. While unimodal methods such as mammography and histopathology analysis achieve strong performance, integrating multimodal data ...
Muhammad Aftab +7 more
core +1 more source
Unlocking the potential of cell‐permeable nanobodies for intracellular applications
FEBS Letters, EarlyView.This review outlines current strategies for delivery of nanobodies into cells and highlights recent examples of functional applications. By addressing and manipulating intracellular targets, these molecules hold great potential for treating cancer, respiratory diseases and neurological disorders, making this a vibrant area of research in the biomedical
Mariona Gené‐Verdés +2 more
wiley +1 more source
Telephone conversation impairs sustained visual attention via a central bottleneck [PDF]
, 2008 Recent research has shown that holding telephone conversations disrupts one's driving ability. We asked whether this effect could be attributed to a visual attention impairment.
Randall Carter +7 more
core +2 more sources
Lightweight Channel Attention for Efficient CNNs
CoRRAttention mechanisms have become integral to modern convolutional neural networks (CNNs), delivering notable performance improvements with minimal computational overhead. However, the efficiency accuracy trade off of different channel attention designs remains underexplored.
Prem Babu Kanaparthi +1 more
openaire +3 more sources
Molecular Oncology, EarlyView.The proposed mechanism of action for the CDK12/13 inhibitor and cyclin K degrader, CT7439. CDK12/13 inhibition interrupts transcription elongation, leading to increased DNA damage that results in cell death. This agent is a potentially novel treatment option for patients with colorectal cancer. Created in BioRender. Cyclin‐dependent kinase (CDK) 12 and
Wylie K. Watlington +10 more
wiley +1 more source
Channel-Attentive Graph Neural Networks
2024 IEEE International Conference on Data Mining (ICDM)Graph Neural Networks (GNNs) set the state-of-the-art in representation learning for graph-structured data. They are used in many domains, from online social networks to complex molecules. Most GNNs leverage the message-passing paradigm and achieve strong performances on various tasks.
Tugrul Hasan Karabulut, Inci M. Baytas
openaire +4 more sources
Channel-spatial attention network for fewshot classification
PLOS ONE, 2019 Learning a powerful representation for a class with few labeled samples is a challenging problem. Although some state-of-the-art few-shot learning algorithms perform well based on meta-learning, they only focus on novel network architecture and fail to take advantage of the knowledge of every classification task. In this paper, to accomplish this goal,
Yan Zhang, Min Fang, Nian Wang
openaire +4 more sources
Molecular Oncology, EarlyView.Drug resistance limits treatment success in a subset of lung cancers driven by ROS1 gene alterations. Using patient‐derived cells and computer simulations, we studied three key mutations and how they affect five targeted drugs. The mutations reduced drug effectiveness in different ways by altering protein structure and behavior.
Farhan Ul Haq +8 more
wiley +1 more source
Structure comparison of the different order of channel and spatial attention.
, 2023 (a) is CBAM attention mechanism including a channel attention and spatial attention mechanism. (b) is the mechanism of two channel followed by two spatial attentions. (c) is the mechanism of two spatial attentions followed by two channel attentions.
Qihang Li (8931125) +5 more
core +1 more source
TACOformer:Token-channel compounded Cross Attention for Multimodal Emotion Recognition [PDF]
, 2023 Recently, emotion recognition based on physiological signals has emerged as a field with intensive research. The utilization of multi-modal, multi-channel physiological signals has significantly improved the performance of emotion recognition systems ...
Li, Xinda
core +1 more source