AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Retinal Vessel Segmentation by a Transformer-U-Net Hybrid Model With Dual-Path Decoder.

IEEE journal of biomedical and health informatics
This paper introduces an effective and efficient framework for retinal vessel segmentation. First, we design a Transformer-CNN hybrid model in which a Transformer module is inserted inside the U-Net to capture long-range interactions. Second, we desi...

Neural Networks Based Smart E-Health Application for the Prediction of Tuberculosis Using Serverless Computing.

IEEE journal of biomedical and health informatics
The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-...

Self-Supervised Image Denoising of Third Harmonic Generation Microscopic Images of Human Glioma Tissue by Transformer-Based Blind Spot (TBS) Network.

IEEE journal of biomedical and health informatics
Third harmonic generation (THG) microscopy shows great potential for instant pathology of brain tumor tissue during surgery. However, due to the maximal permitted exposure of laser intensity and inherent noise of the imaging system, the noise level o...

Attention-Based MultiOffset Deep Learning Reconstruction of Chemical Exchange Saturation Transfer (AMO-CEST) MRI.

IEEE journal of biomedical and health informatics
One challenge of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is the long scan time due to multiple acquisitions of images at different saturation frequency offsets. k-space under-sampling strategy is commonly used to...

Boundary-Aware Gradient Operator Network for Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Medical image segmentation is a crucial task in computer-aided diagnosis. Although convolutional neural networks (CNNs) have made significant progress in the field of medical image segmentation, the convolution kernels of CNNs are optimized from rand...

Automatically Extracting and Utilizing EEG Channel Importance Based on Graph Convolutional Network for Emotion Recognition.

IEEE journal of biomedical and health informatics
Graph convolutional network (GCN) based on the brain network has been widely used for EEG emotion recognition. However, most studies train their models directly without considering network dimensionality reduction beforehand. In fact, some nodes and ...

Riemannian Locality Preserving Method for Transfer Learning With Applications on Brain-Computer Interface.

IEEE journal of biomedical and health informatics
Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and appli...

ST-Phys: Unsupervised Spatio-Temporal Contrastive Remote Physiological Measurement.

IEEE journal of biomedical and health informatics
Remote photoplethysmography (rPPG) is a non-contact method that employs facial videos for measuring physiological parameters. Existing rPPG methods have achieved remarkable performance. However, the success mainly profits from supervised learning ove...

Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network.

IEEE journal of biomedical and health informatics
This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by int...

MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction.

IEEE journal of biomedical and health informatics
This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-mo...