Parkinson's disease ranks as the second most prevalent neurological disorder after Alzheimer's disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson's disease (PD) detection using MR images. However, CNN models gen...
Neural networks : the official journal of the International Neural Network Society
Apr 28, 2025
As an economically feasible approach for hyperspectral image (HSI) super-resolution, fusing HSI with multispectral image (MSI) utilizes the complementary nature of cross-modality information. Given the common presence of repetitive textures and struc...
International journal of neural systems
Apr 28, 2025
Due to the differences in size, shape, and location of brain tumors, brain tumor segmentation differs greatly from that of other organs. The purpose of brain tumor segmentation is to accurately locate and segment tumors from MRI images to assist doct...
The reliance on computationally intensive U-Net and Transformer architectures significantly limits their accessibility in low-resource environments, creating a technological divide that hinders global healthcare equity, especially in medical diagnost...
Computer methods and programs in biomedicine
Apr 28, 2025
OBJECTIVE: Artificial intelligence (AI) models are effective for analyzing high-quality slit-lamp images but often face challenges in real-world clinical settings due to image variability. This study aims to develop and evaluate a hybrid AI-based ima...
Precise segmentation and uncertainty estimation are crucial for error identification and correction in medical diagnostic assistance. Existing methods mainly rely on pixel-wise uncertainty estimations. They (1) neglect the global context, leading to ...
Brain lesion segmentation is challenging in medical image analysis, aiming to delineate lesion regions precisely. Deep learning (DL) techniques have recently demonstrated promising results across various computer vision tasks, including semantic segm...
Digital images have become a powerful tool for developing analytical methods in food quality control. Unlike conventional analytical signals, images can be processed to extract relevant chemical information, with chemometric techniques enhancing thei...
Traditional medical image generation often lacks patient-specific clinical information, limiting its clinical utility despite enhancing downstream task performance. In contrast, medical image translation precisely converts images from one modality to...
Computer methods and programs in biomedicine
Apr 27, 2025
BACKGROUND AND OBJECTIVE: Semi-supervised medical image segmentation is a class of machine learning paradigms for segmentation model training and inference using both labeled and unlabeled medical images, which can effectively reduce the data labelin...
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