AIMC Topic:
Supervised Machine Learning

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Improving Endoscopy Lesion Classification Using Self-Supervised Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
In this work, we assess the impact of self-supervised learning (SSL) approaches on the detection of gastritis atrophy (GA) and intestinal metaplasia (IM) conditions. GA and IM are precancerous gastric lesions. Detecting these lesions is crucial to in...
Metaplasia Deep Learning Supervised Machine Learning Humans Algorithms Endoscopy
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Enhancing Model Generalizability In Parkinson's Disease Automatic Assessment: A Semi-Supervised Approach Across Independent Experiments.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Machine learning in Parkinson's disease assessment uses data from clinically-coded movements, such as finger tapping, to objectively measure motor impairment. Video-based models showed promise in several experiments, but the lack of a unified test be...
Parkinson Disease Humans Machine Learning Supervised Machine Learning Algorithms
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Dual Prototypical Self-Supervised Learning for One-shot Medical Image Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Medical image segmentation using deep learning typically requires a large quantity of well-annotated data. However, the acquisition of pixel-level annotations is arduous and expensive, often requiring the expertise of experienced medical professional...
Deep Learning Image Processing, Computer-Assisted Supervised Machine Learning Humans Algorithms Diagnostic Imaging
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COINS: Counting Cones Using Inpainting Based Self-supervised Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
A novel approach for "COunting cones using IN-painting based Self-supervised learning (SSL)"(COINS), in wide field-of-view, low-resolution Adaptive Optics (AO) images is described. The proposed approach is applied to a dataset of 4°×4° AO images capt...
Algorithms Image Processing, Computer-Assisted Supervised Machine Learning Humans
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ECG Abnormality Detection Using MIMIC-IV-ECG Data Via Supervised Contrastive Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Electrocardiogram data provide a tremendous opportunity for the detection of various types of cardiac arrhythmia. Recent advancement in ubiquitous wearable devices with incorporated ECG sensors offers an opportunity for a real-time monitoring system ...
Humans Arrhythmias, Cardiac Signal Processing, Computer-Assisted Atrial Fibrillation Algorithms Supervised Machine Learning Electrocardiography
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Contrastive Self-supervised EEG Representation Learning for Emotion Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Self-supervised learning provides an effective approach to leverage a large amount of unlabeled data. Numerous previous studies have indicated that applying self-supervision to physiological signals can yield better representations of the signals. In...
Supervised Machine Learning Electroencephalography Signal Processing, Computer-Assisted Emotions Humans Algorithms
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Instance-Wise MRI Reconstruction Based on Self-Supervised Implicit Neural Representation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Accelerated MRI involves a trade-off between sampling sufficiency and acquisition time. Supervised deep learning methods have shown great success in MRI reconstruction from under-sampled measurements, but they typically require a large set of fully-s...
Deep Learning Image Processing, Computer-Assisted Brain Reproducibility of Results Magnetic Resonance Imaging Supervised Machine Learning Humans Neural Networks, Computer Algorithms
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A Three-Stage Semi-Supervised Learning Approach to Spine Image Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Spine segmentation in computed tomography (CT) images is critical for automatic analysis, especially when focusing on varied spinal anatomy. Despite having comprehensive annotations for normal vertebrae, many datasets do not encompass labeled fractur...
Humans Algorithms Spine Imaging, Three-Dimensional Supervised Machine Learning Image Processing, Computer-Assisted Tomography, X-Ray Computed
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UPMatch: Enhancing Semi-Supervised Medical Image Classification through Contrastive Learning with Unreliable Pseudo Labels.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Pseudo-labeling based semi-supervised learning (SSL) framework has proven highly successful in medical image analysis (MIA) by addressing the problem of a shortage of labeled samples. However, the existing SSL methods use a fixed or flexible confiden...
Supervised Machine Learning Humans Image Processing, Computer-Assisted Algorithms Diagnostic Imaging
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Weakly Supervised Deep Learning in Radiology.

Radiology Jul 1, 2024
Deep learning (DL) is currently the standard artificial intelligence tool for computer-based image analysis in radiology. Traditionally, DL models have been trained with strongly supervised learning methods. These methods depend on reference standard...
Humans Supervised Machine Learning Deep Learning Image Interpretation, Computer-Assisted Radiology
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