AIMC Topic:
Supervised Machine Learning
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Predicting cognitive function among Chinese community-dwelling older adults: A supervised machine learning approach.
Preventive medicine
Jul 1, 2025
OBJECTIVE: Identifying cognitive impairment early enough could support timely intervention of cognitive impairment and facilitate successful cognitive aging. We aimed to build more precise prediction models for cognitive function using less variable ...
Semi-Supervised Gland Segmentation via Feature-Enhanced Contrastive Learning and Dual-Consistency Strategy.
IEEE journal of biomedical and health informatics
Jun 1, 2025
In the field of gland segmentation in histopathology, deep-learning methods have made significant progress. However, most existing methods not only require a large amount of high-quality annotated data but also tend to confuse the internal of the gla...
Exploring Unbiased Activation Maps for Weakly Supervised Tissue Segmentation of Histopathological Images.
IEEE transactions on medical imaging
Jun 1, 2025
Tissue segmentation in histopathological images plays a crucial role in computational pathology, owing to its significant potential to indicate the prognosis of cancer patients. Presently, numerous Weakly Supervised Semantic Segmentation (WSSS) metho...
Coarse for Fine: Bounding Box Supervised Thyroid Ultrasound Image Segmentation Using Spatial Arrangement and Hierarchical Prediction Consistency.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of low-level features, which are easily corrupted in thyroid ultrasoun...
Score-Based Diffusion Models With Self-Supervised Learning for Accelerated 3D Multi-Contrast Cardiac MR Imaging.
IEEE transactions on medical imaging
Jun 1, 2025
Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate 3D-MC-CMR acquisition by a novel method based on score-based diffusion ...
Semi-Supervised PARAFAC2 Decomposition for Computational Phenotyping Using Electronic Health Records.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Computational phenotyping uses data mining methods to extract clusters of clinical descriptors, known as phenotypes, from electronic health records (EHR). Tensor factorization methods are very effective in extracting meaningful patterns and have beco...
TGAP-Net: Twin Graph Attention Pseudo-Label Generation for Weakly Supervised Semantic Segmentation.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Multilabel pathological tissue segmentation is a vital task in computational pathology that aims to semantically segment different tissues within pathological images. Fully and weakly supervised models have demonstrated impressive performances in thi...
SNER: Semi-Supervised Named Entity Recognition for Large Volume of Diabetes Data.
IEEE journal of biomedical and health informatics
Jun 1, 2025
The medical literature and records on diabetes provide crucial resources for diabetes prevention and treatment. However, extracting entities from these textual diabetes data is crucial but challenging. Named entity recognition (NER) - an important co...
MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Accurately diagnosing chronic kidney disease requires pathologists to assess the structure of multiple tissues under different stains, a process that is time-consuming and labor-intensive. Current AI-based methods for automatic structure assessment, ...
Predicting thyroid cancer recurrence using supervised CatBoost: A SHAP-based explainable AI approach.
Medicine
May 30, 2025
Recurrence prediction in well-differentiated thyroid cancer remains a clinical challenge, necessitating more accurate and interpretable predictive models. This study investigates the use of a supervised CatBoost classifier to predict recurrence in we...
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