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...
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...
Neural networks : the official journal of the International Neural Network Society
May 6, 2025
Pseudo supervision has demonstrated empirical success in semi-supervised segmentation tasks by effectively leveraging unlabeled data, but it unavoidably encounters the problem caused by noisy pseudo labels. Existing methods against noisy pseudo label...
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...
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...
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...
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...
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...
IEEE transactions on neural networks and learning systems
May 2, 2025
Recent deep learning models can efficiently combine inputs from different modalities (e.g., images and text) and learn to align their latent representations or to translate signals from one domain to another (as in image captioning or text-to-image g...
BMC medical informatics and decision making
May 1, 2025
BACKGROUND AND OBJECTIVE: This study has two main objectives. First, to evaluate a feature selection methodology based on SEQENS, an algorithm for identifying relevant variables. Second, to validate machine learning models that predict the risk of co...
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