AIMC Topic: Humans

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Improved region proposal network for enhanced few-shot object detection.

Neural networks : the official journal of the International Neural Network Society
Despite significant success of deep learning in object detection tasks, the standard training of deep neural networks requires access to a substantial quantity of annotated images across all classes. Data annotation is an arduous and time-consuming e...

Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular diseases (CVDs). It is important to consider both temporal and spatial features in ECG analysis to improve automated CVDs diagnosis. Significant progre...

Machine Learning for Targeted Advance Care Planning in Cancer Patients: A Quality Improvement Study.

Journal of pain and symptom management
CONTEXT: Prognostication challenges contribute to delays in advance care planning (ACP) for patients with cancer near the end of life (EOL).

Prediction of patient cooperation before orthodontic treatment: Handwriting and artificial intelligence.

Journal of the World federation of orthodontists
BACKGROUND: The purpose of this study was to compare the success of various convolutional neural network (CNN) models trained with handwriting samples in predicting patient cooperation.

PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.

Physical and engineering sciences in medicine
The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for p...

Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training.

Medical physics
BACKGROUND: Deep learning is the primary method for conducting automated analysis of SPECT bone scintigrams. The lack of available large-scale data significantly hinders the development of well-performing deep learning models, as the performance of a...

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks.

Journal of neuroscience methods
BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distribut...

Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring.

Speech synthesis from three-axis accelerometer signals using conformer-based deep neural network.

Computers in biology and medicine
Silent speech interfaces (SSIs) have emerged as innovative non-acoustic communication methods, and our previous study demonstrated the significant potential of three-axis accelerometer-based SSIs to identify silently spoken words with high classifica...

m5C-Seq: Machine learning-enhanced profiling of RNA 5-methylcytosine modifications.

Computers in biology and medicine
Epigenetic modifications, particularly RNA methylation and histone alterations, play a crucial role in heredity, development, and disease. Among these, RNA 5-methylcytosine (m5C) is the most prevalent RNA modification in mammalian cells, essential fo...