AIMC Topic: Datasets as Topic

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Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach.

Sensors (Basel, Switzerland) Feb 28, 2025
BACKGROUND: Infant brain tissue segmentation from MRI data is a critical task in medical imaging, particularly challenging due to the evolving nature of tissue contrasts in the early months of life. The difficulty increases as gray matter (GM) and wh...
White Matter Humans Brain Image Processing, Computer-Assisted Magnetic Resonance Imaging Datasets as Topic Gray Matter Cerebrospinal Fluid Infant Spatio-Temporal Analysis Convolutional Neural Networks Imaging, Three-Dimensional Deep Learning
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FusionNet: Dual input feature fusion network with ensemble based filter feature selection for enhanced brain tumor classification.

Brain research Feb 17, 2025
Brain tumors pose a significant threat to human health, require a precise and quick diagnosis for effective treatment. However, achieving high diagnostic accuracy with traditional methods remains challenging due to the complex nature of brain tumors....
Image Processing, Computer-Assisted Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Brain Neoplasms Deep Learning Datasets as Topic Brain
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Importance of dataset design in developing robust U-Net models for label-free cell morphology evaluation.

Journal of bioscience and bioengineering Feb 10, 2025
Advances in regenerative medicine highlighted the need for label-free cell image analysis to replace conventional microscopic observation for non-invasive cell quality evaluation. Image-based evaluation provides an efficient, quantitative, and automa...
Neural Networks, Computer Animals Image Processing, Computer-Assisted Humans Cell Shape Regenerative Medicine Datasets as Topic
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A Deep Reinforcement Learning-Based Feature Selection Method for Invasive Disease Event Prediction Using Imbalanced Follow-Up Data.

IEEE journal of biomedical and health informatics Feb 10, 2025
The machine learning-based model is a promising paradigm for predicting invasive disease events (iDEs) in breast cancer. Feature selection (FS) is an essential preprocessing technique employed to identify the pertinent features for the prediction mod...
Breast Neoplasms Neoplasm Invasiveness Reinforcement Machine Learning Datasets as Topic Prognosis Female Humans
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Generative and contrastive graph representation learning with message passing.

Neural networks : the official journal of the International Neural Network Society Feb 6, 2025
Self-supervised graph representation learning (SSGRL) has emerged as a promising approach for graph embeddings because it does not rely on manual labels. SSGRL methods are generally divided into generative and contrastive approaches. Generative metho...
Supervised Machine Learning Datasets as Topic Graph Neural Networks Autoencoder Generative Artificial Intelligence
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The Data Artifacts Glossary: a community-based repository for bias on health datasets.

Journal of biomedical science Feb 4, 2025
BACKGROUND: The deployment of Artificial Intelligence (AI) in healthcare has the potential to transform patient care through improved diagnostics, personalized treatment plans, and more efficient resource management. However, the effectiveness and fa...
Datasets as Topic Artificial Intelligence Bias Humans
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Multi-modal dataset creation for federated learning with DICOM-structured reports.

International journal of computer assisted radiology and surgery Feb 3, 2025
Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging...
Humans Tomography, X-Ray Computed Datasets as Topic Radiology Information Systems Federated Learning Deep Learning Electrocardiography
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The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets.

JMIR medical informatics Jan 28, 2025
BACKGROUND: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients'...
Brazil Humans Datasets as Topic Machine Learning
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Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.

Biomedical physics & engineering express Jan 22, 2025
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing b...
Gray Matter White Matter Prediction Methods, Machine Spatial Analysis Neuroimaging Neural Networks, Computer Predictive Value of Tests Alzheimer Disease Cognitive Dysfunction Cerebrospinal Fluid Genetic Predisposition to Disease Datasets as Topic Humans Genetic Testing Magnetic Resonance Imaging
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Semantic search helper: A tool based on the use of embeddings in multi-item questionnaires as a harmonization opportunity for merging large datasets - A feasibility study.

European psychiatry : the journal of the Association of European Psychiatrists Jan 20, 2025
BACKGROUND: Recent advances in natural language processing (NLP), particularly in language processing methods, have opened new avenues in semantic data analysis. A promising application of NLP is data harmonization in questionnaire-based cohort studi...
Semantics Humans Surveys and Questionnaires Natural Language Processing Feasibility Studies Datasets as Topic
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