Whole slide images (WSI), due to their gigabyte-scale size and ultra-high resolution, play a significant role in diagnostic pathology. However, the enormous data size makes it difficult to directly input these images into image processing units (GPU)...
Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is considered the in vivo reference standard for assessing infarct size (IS) and microvascular obstruction (MVO) in ST-elevation myocardial infarction (STEMI) patients. Howeve...
Confounding factors inherent in medical images can significantly impact the causal exploration capabilities of deep learning models, resulting in compromised accuracy and diminished generalization performance. In this paper, we present an innovative ...
OBJECTIVE: Interest has grown in combining radiology, pathology, genomic, and clinical data to improve the accuracy of diagnostic and prognostic predictions toward precision health. However, most existing works choose their datasets and modeling appr...
Medical image segmentation is a pivotal task within the realms of medical image analysis and computer vision. While current methods have shown promise in accurately segmenting major regions of interest, the precise segmentation of boundary areas rema...
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 ...
To explore the capabilities of race/ethnicity and gender prediction algorithms in uncovering patterns of authorship distribution in scientific paper submissions to a major peer-reviewed scientific journal (), we analyzed 17 667 manuscript submissions...
Computer methods and programs in biomedicine
Jul 1, 2025
OBJECTIVES: Achieving efficient analysis of multiple pathological indicators has great significance for breast cancer prognosis and therapeutic decision-making. In this study, we aim to explore a deep multi-task learning (MTL) framework for collabora...
Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image registration due to their speed and ability to incorporate weak label supervision at training time. However, existing DLIR methods forego many of the benefi...
Training neural networks (NNs) to behave as model predictive control (MPC) algorithms is an effective way to implement them in constrained embedded devices. By collecting large amounts of input-output data, where inputs represent system states and ou...
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