AIMC Topic: Machine Learning

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Machine learning explainability for survival outcome in head and neck squamous cell carcinoma.

International journal of medical informatics
BACKGROUND: Diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC) induces psychological variables and treatment-related toxicity in patients. The evaluation of outcomes is warranted for effective treatment planning and improved dis...

Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy.

International journal of cardiology
BACKGROUND: Takotsubo cardiomyopathy (TC) is an acute heart failure syndrome characterized by transient left ventricular dysfunction, often triggered by stress. Data on risk scores predicting mortality in TC is sparse. We developed a machine-learning...

Decomposition-based multi-scale transformer framework for time series anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Time series anomaly detection is crucial for maintaining stable systems. Existing methods face two main challenges. First, it is difficult to directly model the dependencies of diverse and complex patterns within the sequences. Second, many methods t...

Contrastive graph auto-encoder for graph embedding.

Neural networks : the official journal of the International Neural Network Society
Graph embedding aims to embed the information of graph data into low-dimensional representation space. Prior methods generally suffer from an imbalance of preserving structural information and node features due to their pre-defined inductive biases, ...

Molecular property prediction based on graph contrastive learning with partial feature masking.

Journal of molecular graphics & modelling
Molecular representation learning facilitates multiple downstream tasks such as molecular property prediction (MPP) and drug design. Recent studies have shown great promise in applying self-supervised learning (SSL) to cope with the data scarcity in ...

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...

CBCT radiomics features combine machine learning to diagnose cystic lesions in the jaw.

Dento maxillo facial radiology
OBJECTIVE: The aim of this study was to develop a radiomics model based on cone beam CT (CBCT) to differentiate odontogenic cysts (OCs), odontogenic keratocysts (OKCs), and ameloblastomas (ABs).

Turbulence control in memristive neural network via adaptive magnetic flux based on DLS-ADMM technique.

Neural networks : the official journal of the International Neural Network Society
High-voltage defibrillation for eliminating cardiac spiral waves has significant side effects, necessitating the pursuit of low-energy alternatives for a long time. Adaptive optimization techniques and machine learning methods provide promising solut...

Self-attention fusion and adaptive continual updating for multimodal federated learning with heterogeneous data.

Neural networks : the official journal of the International Neural Network Society
Federated learning (FL) enables collaborative model training without direct data sharing, facilitating knowledge exchange while ensuring data privacy. Multimodal federated learning (MFL) is particularly advantageous for decentralized multimodal data,...

Enhancing few-shot image classification through learnable multi-scale embedding and attention mechanisms.

Neural networks : the official journal of the International Neural Network Society
In the context of few-shot classification, the goal is to train a classifier using a limited number of samples while maintaining satisfactory performance. However, traditional metric-based methods exhibit certain limitations in achieving this objecti...