AIMC Topic: Deep Learning

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A Spatiotemporal Deep Learning Framework for Scalp EEG-Based Automated Pain Assessment in Children.

IEEE transactions on bio-medical engineering
OBJECTIVE: Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment ...

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal.

IEEE transactions on bio-medical engineering
OBJECTIVE: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of...

Towards Efficient Neural Decoder for Dexterous Finger Force Predictions.

IEEE transactions on bio-medical engineering
OBJECTIVE: Dexterous control of robot hands requires a robust neural-machine interface capable of accurately decoding multiple finger movements. Existing studies primarily focus on single-finger movement or rely heavily on multi-finger data for decod...

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.

BMC bioinformatics
BACKGROUND: CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine re...

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning.

Physics in medicine and biology
. Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convo...

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography.

Physics in medicine and biology
Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in contrast-enhanced mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described in(...

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

PLoS computational biology
The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models des...

FPNC Net: A hydrogenation catalyst image recognition algorithm based on deep learning.

PloS one
The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pr...

TASA: Temporal Attention With Spatial Autoencoder Network for Odor-Induced Emotion Classification Using EEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The olfactory system enables humans to smell different odors, which are closely related to emotions. The high temporal resolution and non-invasiveness of Electroencephalogram (EEG) make it suitable to objectively study human preferences for odors. Ef...

Interpretable deep learning insights: Unveiling the role of 1 Gy volume on lymphopenia after radiotherapy in breast cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND: Lymphopenia is known for its significance on poor survivals in breast cancer patients. Considering full dosimetric data, this study aimed to develop and validate predictive models for lymphopenia after radiotherapy (RT) in breast cancer.