AIMC Topic: Supervised Machine Learning

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Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised machine learning.

Science advances Jun 4, 2025
Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent predictors of trial enrollment; however, our understanding of the interactions between these factor...
Brain Neoplasms Patient Selection Male Clinical Trials as Topic Socioeconomic Factors Neoplasm Grading Glioma Middle Aged Supervised Machine Learning Humans Female Aged Adult
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Weakly supervised learning through box annotations for pig instance segmentation.

Scientific reports Jun 4, 2025
Pig instance segmentation is a critical component of smart pig farming, serving as the basis for advanced applications such as health monitoring and weight estimation. However, existing methods typically rely on large volumes of precisely labeled mas...
Algorithms Swine Supervised Machine Learning Animals
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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 ...
Humans Electronic Health Records Supervised Machine Learning Deep Learning
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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...
Humans Algorithms Microscopy, Fluorescence Supervised Machine Learning Databases, Factual Blood Vessels Image Processing, Computer-Assisted
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Efficient few-shot medical image segmentation via self-supervised variational autoencoder.

Medical image analysis May 26, 2025
Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, ...
Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Algorithms Autoencoder Supervised Machine Learning Image Interpretation, 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...
Humans Neural Networks, Computer Eye-Tracking Technology Fixation, Ocular Attention Deep Learning Head Supervised Machine Learning
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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...
Breast Supervised Machine Learning Immunohistochemistry Female Data Mining Humans Breast Neoplasms Image Interpretation, Computer-Assisted
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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...
Supervised Machine Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Brain Deep Learning Humans Algorithms
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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...
Deep Learning Image Processing, Computer-Assisted Algorithms Brain Magnetic Resonance Imaging Supervised Machine Learning Humans
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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 Supervised Machine Learning Semantics Echocardiography Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Algorithms
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