AIMC Topic: Supervised Machine Learning

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Trajectory-Ordered Objectives for Self-Supervised Representation Learning of Temporal Healthcare Data Using Transformers: Model Development and Evaluation Study.

JMIR medical informatics Jun 4, 2025
BACKGROUND: The growing availability of electronic health records (EHRs) presents an opportunity to enhance patient care by uncovering hidden health risks and improving informed decisions through advanced deep learning methods. However, modeling EHR ...
Deep Learning Supervised Machine Learning Humans Electronic Health Records
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GatorCLR: Personalized predictions of patient outcomes on electronic health records using self-supervised contrastive graph representation.

Journal of biomedical informatics Jun 2, 2025
OBJECTIVE: Recently, there has been growing interest in analyzing large amounts of Electronic Health Record (EHR) data. Patient outcome prediction is a major area of interest in EHR analysis that focuses on predicting the future health status of pati...
Humans Electronic Health Records Supervised Machine Learning Medical Informatics Algorithms Precision Medicine
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A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts.

PloS one May 27, 2025
Creating a dataset for training supervised machine learning algorithms can be a demanding task. This is especially true for blood vessel segmentation since one or more specialists are usually required for image annotation, and creating ground truth l...
Blood Vessels Microscopy, Fluorescence Humans Supervised Machine Learning Algorithms Databases, Factual Image Processing, Computer-Assisted
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Emergence of human-like attention and distinct head clusters in self-supervised vision transformers: A comparative eye-tracking study.

Neural networks : the official journal of the International Neural Network Society May 21, 2025
Visual attention models aim to predict human gaze behavior, yet traditional saliency models and deep gaze prediction networks face limitations. Saliency models rely on handcrafted low-level visual features, often failing to capture human gaze dynamic...
Neural Networks, Computer Eye-Tracking Technology Deep Learning Humans Head Supervised Machine Learning Fixation, Ocular Attention
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Supervised Information Mining From Weakly Paired Images for Breast IHC Virtual Staining.

IEEE transactions on medical imaging May 6, 2025
Immunohistochemistry (IHC) examination is essential to determine the tumour subtypes, provide key prognostic factors, and develop personalized treatment plans for breast cancer. However, compared to Hematoxylin and Eosin (H&E) staining, the preparati...
Image Interpretation, Computer-Assisted Humans Supervised Machine Learning Immunohistochemistry Female Breast Neoplasms Data Mining Breast
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PEARL: Cascaded Self-Supervised Cross-Fusion Learning for Parallel MRI Acceleration.

IEEE journal of biomedical and health informatics May 6, 2025
Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, elim...
Humans Magnetic Resonance Imaging Algorithms Supervised Machine Learning Deep Learning Image Processing, Computer-Assisted Brain
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Self-supervised learning for MRI reconstruction through mapping resampled k-space data to resampled k-space data.

Magnetic resonance imaging May 3, 2025
In recent years, significant advancements have been achieved in applying deep learning (DL) to magnetic resonance imaging (MRI) reconstruction, which traditionally relies on fully sampled data. However, real-world clinical scenarios often demonstrate...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Brain Deep Learning Supervised Machine Learning Humans Algorithms
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Semi-Supervised Echocardiography Video Segmentation via Adaptive Spatio-Temporal Tensor Semantic Awareness and Memory Flow.

IEEE transactions on medical imaging May 2, 2025
Accurate segmentation of cardiac structures in echocardiography videos is vital for diagnosing heart disease. However, challenges such as speckle noise, low spatial resolution, and incomplete video annotations hinder the accuracy and efficiency of se...
Heart Image Interpretation, Computer-Assisted Humans Echocardiography Supervised Machine Learning Algorithms Semantics Image Processing, Computer-Assisted
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Learnable Prompting SAM-Induced Knowledge Distillation for Semi-Supervised Medical Image Segmentation.

IEEE transactions on medical imaging May 2, 2025
The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust g...
Image Processing, Computer-Assisted Supervised Machine Learning Humans Algorithms
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Role Exchange-Based Self-Training Semi-Supervision Framework for Complex Medical Image Segmentation.

IEEE transactions on neural networks and learning systems May 2, 2025
Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images ba...
Humans Neural Networks, Computer Algorithms Reproducibility of Results Image Processing, Computer-Assisted Deep Learning Supervised Machine Learning Imaging, Three-Dimensional
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