Results 221 to 230 of about 115,711 (260)

Boosted unsupervised feature selection for tumor gene expression profiles

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi, Kaixiang Yang, Mengzhi Wang, Zhiwen Yu, Huanqiang Zeng, Yang Hu   +5 more
wiley   +1 more source

Multiscale tumor characterization in histopathology via self-distilled transformers and topology-aware visual encoding. [PDF]

Sci Rep
Sardar TH, Naresh P, Hassan SM, Kulkarni P, T M R, Ahamed Shafeeq BM.   +5 more
europepmc   +1 more source

Brain‐RetinaNet: Detection of Brain Tumour Using an Improved RetinaNet in Magnetic Resonance Imaging

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Brain tumours disrupt the normal functioning of the brain and, if left untreated, can invade surrounding tissues, blood vessels, and nerves, posing a severe threat. Consequently, early detection is crucial to prevent tragic outcomes. Distinguishing brain tumours through manual detection poses a significant challenge given their diverse ...
Rashid Iqbal, Rabbia Mahum, Mohamed Sharaf, Javed Ali Khan   +3 more
wiley   +1 more source

Feature level quantitative ultrasound and CT information fusion to predict the outcome of head & neck cancer radiotherapy treatment: Enhanced principal component analysis. [PDF]

Med Phys
Moslemi A, Safakish A, Sannachi L, Alberico D, Czarnota GJ.   +4 more
europepmc   +1 more source

MVI‐Depth: Multi‐View Indoor Depth Estimation Based on the Fusion of Semantic Information

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Compared to monocular depth estimation, multi‐view depth estimation often yields more accurate results. However, traditional multi‐view depth estimation methods often fail to leverage semantic information fully and struggle to effectively fuse information from multiple views, leading to suboptimal prediction performance in challenging ...
Ying Zhu, Buyun Chen, Hong Liu, Xia Li
wiley   +1 more source

Federated two-edge graph attention network with weighted global aggregation for electricity consumption demand forecasting. [PDF]

Sci Rep
Yang M, Ren J, Zeng L, Yang X, Li S, Ai Y, Zheng K.   +6 more
europepmc   +1 more source

Physics‐Driven Deep Neural Networks for Solving the Optimal Transport Problem Associated With the Monge–Ampère Equation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Monge–Ampère equations (MAEs) are fully nonlinear second‐order partial differential equations (PDEs), which are closely related to various fields including optimal transport (OT) theory, geometrical optics and affine geometry. Despite their significance, MAEs are extremely challenging to solve.
Xinghua Pan, Zexin Feng, Kang Yang
wiley   +1 more source

Deep learning for atrial electrogram estimation: toward non-invasive arrhythmia mapping using variational autoencoders. [PDF]

Front Physiol
Gutiérrez-Fernández M, López-Linares K, Fambuena-Santos C, Guillem MS, Climent AM, Barquero-Pérez Ó.   +5 more
europepmc   +1 more source

A Bridge Transformer Network With Deep Graph Convolution for Hyperspectral Image Classification

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Transformers have been widely applied to hyperspectral image classification, leveraging their self‐attention mechanism for powerful global modelling. However, two key challenges remain as follows: excessive memory and computational costs from calculating correlations between all tokens (especially as image size or spectral bands increase) and ...
Yuquan Gan, Siyu Wu, Chang Su, Nan Xiang, Zhijie Xu, Yushan Pan   +5 more
wiley   +1 more source