Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Dec 31, 2024
The coffee-ring effect, involving spontaneous solute separation, has demonstrated promising potential in the context of patient serum analysis. In this study, an approach leveraging the coffee-ring-based analyte redistribution was developed for spect...
The autonomous and active Long-Interspersed Element-1 (LINE-1, L1) and the non-autonomous Alu retrotransposon elements, contributing to 30% of the human genome, are the most abundant repeated sequences. With more than 90% of their sequences being met...
This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning CT (CE-pCT) scans using a deep learning model. Unlike previous studies, which often lacked sufficie...
This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breas...
Technology and health care : official journal of the European Society for Engineering and Medicine
Dec 25, 2024
Breast Cancer (BC) is a predominant form of cancer diagnosed in women and one of the deadliest diseases. The important cause of death owing to the cancer amongst women is BC. However, the existing ML techniques are very challenge evaluate the perform...
Journal of research in health sciences
Dec 25, 2024
BACKGROUND: Breast cancer (BC) is the most common cancer in women, and it is important to identify models that can accurately predict mortality in patients with this cancer. The aim of the present study was to use the elastic net regression and artif...
BACKGROUND: Radiomic features and deep features are both vitally helpful for the accurate prediction of tumor information in breast ultrasound. However, whether integrating radiomic features and deep features can improve the prediction performance of...
Deep-learning-based models have achieved state-of-the-art breast cancer risk (BCR) prediction performance. However, these models are highly complex, and the underlying mechanisms of BCR prediction are not fully understood. Key questions include wheth...
PURPOSE: Toxicity to systemic cancer treatment represents a major anxiety for patients and a challenge to treatment plans. We aimed to develop machine learning algorithms for the upfront prediction of an individual's risk of experiencing treatment-re...
PURPOSE: To evaluate the feasibility of multiplexed sensitivity-encoding (MUSE) with deep learning-based reconstruction (DLR) for breast imaging in comparison with conventional diffusion-weighted imaging (DWI) and MUSE alone.