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, ...
low-dose computed tomography (LDCT) images suffer from severe noise due to reduced radiation exposure. Most existing deep learning-based denoising methods require supervised learning with paired training data that is difficult to obtain. To address t...
Neuroscience experiments and devices are generating unprecedented volumes of data, but analyzing and validating them presents practical challenges, particularly in annotation. While expert annotation remains the gold standard, it is time consuming to...
OBJECTIVE: Patients with growth hormone (GH)-secreting pituitary adenomas (PAs) experience various symptoms and comorbidities, which can ultimately lead to increased mortality. This study aimed to develop and validate a machine learning (ML) model fo...
Utilizing thermal infrared facial imaging for fever screening in public spaces has become a common strategy to curb the spread of influenza viruses. However, it is difficult to capture larger number of faces with fever labels, which makes learning fa...
This paper presents a novel method for weakly-supervised semantic segmentation (WSSS) of histology images, where only global image-level labels are employed. We leverage an existing weakly-supervised object localization (WSOL) method to generate clas...
Recently, large pre-trained models (LPM) have achieved great success, which provides rich feature representation for downstream tasks. Pre-training and then fine-tuning is an effective way to utilize LPM. However, the application of LPM in the medica...
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 ...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jul 1, 2025
In abdominal interventional procedures, achieving precise registration of 2D ultrasound (US) frames with 3D computed tomography (CT) scans presents a significant challenge. Traditional tracking methods often rely on high-precision sensors, which can ...
Neural networks : the official journal of the International Neural Network Society
Jul 1, 2025
Semi-supervised learning can significantly boost model performance by leveraging unlabeled data, particularly when labeled data is scarce. However, real-world unlabeled data often contain unseen-class samples, which can hinder the classification of s...
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