AI Medical Compendium

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

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dMIL-Transformer: Multiple Instance Learning Via Integrating Morphological and Spatial Information for Lymph Node Metastasis Classification.

IEEE journal of biomedical and health informatics
Automated classification of lymph node metastasis (LNM) plays an important role in the diagnosis and prognosis. However, it is very challenging to achieve satisfactory performance in LNM classification, because both the morphology and spatial distrib...

Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis.

IEEE journal of biomedical and health informatics
With the development of biotechnology, a large amount of multi-omics data have been collected for precision medicine. There exists multiple graph-based prior biological knowledge about omics data, such as gene-gene interaction networks. Recently, the...

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.

IEEE journal of biomedical and health informatics
Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpre...

An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning.

IEEE journal of biomedical and health informatics
Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG recordings using deep neural networks has been investigated in lit...

Multi-Label Local to Global Learning: A Novel Learning Paradigm for Chest X-Ray Abnormality Classification.

IEEE journal of biomedical and health informatics
Deep neural network (DNN) approaches have shown remarkable progress in automatic Chest X-rays classification. However, existing methods use a training scheme that simultaneously trains all abnormalities without considering their learning priority. In...

Uncertainty-Aware Multi-Dimensional Mutual Learning for Brain and Brain Tumor Segmentation.

IEEE journal of biomedical and health informatics
Existing segmentation methods for brain MRI data usually leverage 3D CNNs on 3D volumes or employ 2D CNNs on 2D image slices. We discovered that while volume-based approaches well respect spatial relationships across slices, slice-based methods typic...

Coarse-Refined Consistency Learning Using Pixel-Level Features for Semi-Supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Pixel-level annotations are extremely expensive for medical image segmentation tasks as both expertise and time are needed to generate accurate annotations. Semi-supervised learning (SSL) for medical image segmentation has recently attracted growing ...

Explanations as a New Metric for Feature Selection: A Systematic Approach.

IEEE journal of biomedical and health informatics
With the extensive use of Machine Learning (ML) in the biomedical field, there was an increasing need for Explainable Artificial Intelligence (XAI) to improve transparency and reveal complex hidden relationships between variables for medical practiti...

Metadata and Image Features Co-Aware Personalized Federated Learning for Smart Healthcare.

IEEE journal of biomedical and health informatics
Recently, artificial intelligence has been widely used in intelligent disease diagnosis and has achieved great success. However, most of the works mainly rely on the extraction of image features but ignore the use of clinical text information of pati...

FSTIF-UNet: A Deep Learning-Based Method Towards Automatic Segmentation of Intracranial Aneurysms in Un-Reconstructed 3D-RA.

IEEE journal of biomedical and health informatics
Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs. However, the process by which clinicians manually recognize and localize IAs is overly labor intensive. This study aims to develop a deep-learni...