AIMC Topic: Deep Learning

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Social anxiety prediction based on ERP features: A deep learning approach.

Journal of affective disorders
BACKGROUND: Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained importance for anxiety detection due to its ability to capture stable and objective neurophysiological ...

Learning clustering-friendly representations via partial information discrimination and cross-level interaction.

Neural networks : the official journal of the International Neural Network Society
Despite significant advances in the deep clustering research, there remain three critical limitations to most of the existing approaches. First, they often derive the clustering result by associating some distribution-based loss to specific network l...

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...

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...

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.

Deep learning permits imaging of multiple structures with the same fluorophores.

Biophysical journal
Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can...