AI Medical Compendium Topic:
Supervised Machine Learning

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Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning.

IEEE journal of biomedical and health informatics
Medical instrument segmentation in 3D ultrasound is essential for image-guided intervention. However, to train a successful deep neural network for instrument segmentation, a large number of labeled images are required, which is expensive and time-co...

Incomplete Label Multiple Instance Multiple Label Learning.

IEEE transactions on pattern analysis and machine intelligence
With increasing data volumes, the bottleneck in obtaining data for training a given learning task is the cost of manually labeling instances within the data. To alleviate this issue, various reduced label settings have been considered including semi-...

Investigating Deep Learning Based Breast Cancer Subtyping Using Pan-Cancer and Multi-Omic Data.

IEEE/ACM transactions on computational biology and bioinformatics
Breast Cancer comprises multiple subtypes implicated in prognosis. Existing stratification methods rely on the expression quantification of small gene sets. Next Generation Sequencing promises large amounts of omic data in the next years. In this sce...

Predicting Drug-Drug Interactions Based on Integrated Similarity and Semi-Supervised Learning.

IEEE/ACM transactions on computational biology and bioinformatics
A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause th...

Predicting Embryo Viability Based on Self-Supervised Alignment of Time-Lapse Videos.

IEEE transactions on medical imaging
With self-supervised learning, both labeled and unlabeled data can be used for representation learning and model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during a fertility trea...

Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data.

IEEE transactions on medical imaging
Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross-domain data is of high importance to further improve model robustness...

Electrocardiogram Signal Classification in the Diagnosis of Heart Disease Based on RBF Neural Network.

Computational and mathematical methods in medicine
Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG d...

Weakly-supervised learning for catheter segmentation in 3D frustum ultrasound.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate and efficient catheter segmentation in 3D ultrasound (US) is essential for ultrasound-guided cardiac interventions. State-of-the-art segmentation algorithms, based on convolutional neural networks (CNNs), suffer from high computational cost ...

Decoding alarm signal propagation of seed-harvester ants using automated movement tracking and supervised machine learning.

Proceedings. Biological sciences
Alarm signal propagation through ant colonies provides an empirically tractable context for analysing information flow through a natural system, with useful insights for network dynamics in other social animals. Here, we develop a methodological appr...

Semi-supervised incremental learning with few examples for discovering medical association rules.

BMC medical informatics and decision making
BACKGROUND: Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful i...