AIMC Topic: Algorithms

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Semi-Supervised Detection Model Based on Adaptive Ensemble Learning for Medical Images.

IEEE transactions on neural networks and learning systems
Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscopes. Moreover, works relying on supervised learning are powerless in the case of ...

Machine learning algorithms to predict depression in older adults in China: a cross-sectional study.

Frontiers in public health
OBJECTIVE: The 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among China's older adult population and to determine the noteworthy aspects resulting in depression.

Empirical analysis on retinal segmentation using PSO-based thresholding in diabetic retinopathy grading.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: Diabetic retinopathy (DR) is associated with long-term diabetes and is a leading cause of blindness if it is not diagnosed early. The rapid growth of deep learning eases the clinicians' DR diagnosing procedure. It automatically extracts t...

DIFLF: A domain-invariant features learning framework for single-source domain generalization in mammogram classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Single-source domain generalization (SSDG) aims to generalize a deep learning (DL) model trained on one source dataset to multiple unseen datasets. This is important for the clinical applications of DL-based models to breast...

A novel swarm budorcas taxicolor optimization-based multi-support vector method for transformer fault diagnosis.

Neural networks : the official journal of the International Neural Network Society
To address the challenge of low recognition accuracy in transformer fault detection, a novel method called swarm budorcas taxicolor optimization-based multi-support vector (SBTO-MSV) is proposed. Firstly, a multi-support vector (MSV) model is propose...

Semi-supervised medical image segmentation via weak-to-strong perturbation consistency and edge-aware contrastive representation.

Medical image analysis
Despite that supervised learning has demonstrated impressive accuracy in medical image segmentation, its reliance on large labeled datasets poses a challenge due to the effort and expertise required for data acquisition. Semi-supervised learning has ...

A smooth gradient approximation neural network for general constrained nonsmooth nonconvex optimization problems.

Neural networks : the official journal of the International Neural Network Society
Nonsmooth nonconvex optimization problems are pivotal in engineering practice due to the inherent nonsmooth and nonconvex characteristics of many real-world complex systems and models. The nonsmoothness and nonconvexity of the objective and constrain...

DCS-RISR: Dynamic channel splitting for efficient real-world image super-resolution.

Neural networks : the official journal of the International Neural Network Society
Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradatio...

A discrete convolutional network for entity relation extraction.

Neural networks : the official journal of the International Neural Network Society
Relation extraction independently verifies all entity pairs in a sentence to identify predefined relationships between named entities. Because these entity pairs share the same contextual features of a sentence, they lead to a complicated semantic st...

ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism.

Neural networks : the official journal of the International Neural Network Society
Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced...