AIMC Topic: Deep Learning

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Radiation and contrast dose reduction in coronary CT angiography for slender patients with 70 kV tube voltage and deep learning image reconstruction.

The British journal of radiology
OBJECTIVE: To evaluate the radiation and contrast dose reduction potential of combining 70 kV with deep learning image reconstruction (DLIR) in coronary computed tomography angiography (CCTA) for slender patients with body-mass-index (BMI) ≤25 kg/m2.

Diverse Teacher-Students for deep safe semi-supervised learning under class mismatch.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised learning can significantly boost model performance by leveraging unlabeled data, particularly when labeled data is scarce. However, real-world unlabeled data often contain unseen-class samples, which can hinder the classification of s...

Mathematical expression exploration with graph representation and generative graph neural network.

Neural networks : the official journal of the International Neural Network Society
Symbolic Regression (SR) methods in tree representations have exhibited commendable outcomes across Genetic Programming (GP) and deep learning search paradigms. Nonetheless, the tree representation of mathematical expressions occasionally embodies re...

A novel deep transfer learning method based on explainable feature extraction and domain reconstruction.

Neural networks : the official journal of the International Neural Network Society
Although deep transfer learning has made significant progress, its "black-box" nature and unstable feature adaptation remain key obstacles. This study proposes a multi-stage deep transfer learning method, called XDTL, which combines explainable featu...

Deformation-invariant neural network and its applications in distorted image restoration and analysis.

Neural networks : the official journal of the International Neural Network Society
Images degraded by geometric distortions pose a significant challenge to imaging and computer vision tasks such as object recognition. Deep learning-based imaging models usually fail to give accurate performance for geometrically distorted images. In...

A novel self-supervised graph clustering method with reliable semi-supervision.

Neural networks : the official journal of the International Neural Network Society
Cluster analysis, as a core technique in unsupervised learning, has widespread applications. With the increasing complexity of data, deep clustering, which integrates the advantages of deep learning and traditional clustering algorithms, demonstrates...

LUNETR: Language-Infused UNETR for precise pancreatic tumor segmentation in 3D medical image.

Neural networks : the official journal of the International Neural Network Society
The identification of early micro-lesions and adjacent blood vessels in CT scans plays a pivotal role in the clinical diagnosis of pancreatic cancer, considering its aggressive nature and high fatality rate. Despite the widespread application of deep...

A shape composition method for named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Large language models (LLMs) roughly encode a sentence into a dense representation (a vector), which mixes up the semantic expression of all named entities within a sentence. So the decoding process is easily overwhelmed by sentence-specific informat...

An improved Artificial Protozoa Optimizer for CNN architecture optimization.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a novel neural architecture search (NAS) method called MAPOCNN, which leverages an enhanced version of the Artificial Protozoa Optimizer (APO) to optimize the architecture of Convolutional Neural Networks (CNNs). The APO is ...

Deep Huber quantile regression networks.

Neural networks : the official journal of the International Neural Network Society
Typical machine learning regression applications aim to report the mean or the median of the predictive probability distribution, via training with a squared or an absolute error scoring function. The importance of issuing predictions of more functio...