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Cardiac masses encompass a diverse range of benign and malignant tumors as well as pseudotumors. Accurate histologic identification is essential for guiding appropriate treatment, yet the diagnostic process remains challenging. Although biopsy is traditionally the diagnostic gold standard, its invasive nature and associated risks limit its application. A noninvasive multimodality imaging approach has recently emerged as an alternative, but standardized protocols and supporting evidence are still lacking. Echocardiography is typically the initial imaging modality, with cardiac magnetic resonance recognized as the noninvasive diagnostic gold standard. Cardiac computed tomography provides complementary data to aid in diagnosis and management, while positron emission tomography serves as a third-level imaging option. This state-of-the-art review highlights the role of current multimodality imaging techniques in diagnosing and managing cardiac masses and explores future directions for their applications.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11711820 | PMC |
http://dx.doi.org/10.1016/j.jaccao.2024.09.006 | DOI Listing |
Neural Netw
September 2025
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:
Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
3D shape defect detection plays an important role in autonomous industrial inspection. However, accurate detection of anomalies remains challenging due to the complexity of multimodal sensor data, especially when both color and structural information are required. In this work, we propose a lightweight inter-modality feature prediction framework that effectively utilizes multimodal fused features from the inputs of RGB, depth and point clouds for efficient 3D shape defect detection.
View Article and Find Full Text PDFNeural Netw
September 2025
Shanghai Maritime University, Shanghai, 201306, China. Electronic address:
Cross-modal hashing aims to leverage hashing functions to map multimodal data into a unified low-dimensional space, realizing efficient cross-modal retrieval. In particular, unsupervised cross-modal hashing methods attract significant attention for not needing external label information. However, in the field of unsupervised cross-modal hashing, there are several pressing issues to address: (1) how to facilitate semantic alignment between modalities, and (2) how to effectively capture the intrinsic relationships between data, thereby constructing a more reliable affinity matrix to assist in the learning of hash codes.
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September 2025
Doheny Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, California, USA.
Purpose: To report the examination and multimodal imaging findings of a patient with unilateral bull's eye maculopathy.
Methods: A retrospective chart review of a 77-year-old patient with unilateral bull's eye maculopathy who presented to a tertiary retinal practice was performed. The patient's history, visual acuity, examination and multimodal imaging findings over five years of follow-up were described.
J Vis Exp
August 2025
Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology.
We present multimodal confocal Raman micro-spectroscopy (RS) and tomographic phase microscopy (TPM) for quick morpho-chemical phenotyping of human breast cancer cells (MDA-MB-231). Leveraging the non-perturbative nature of these advanced microscopy techniques, we captured detailed morpho-molecular data from living, label-free cells in their native physiological environment. Human bias-free data processing pipelines were developed to analyze hyperspectral Raman images (spanning Raman modes from 600 cm to 1800 cm, which uniquely characterize a wide range of molecular bonds and subcellular structures), as well as morphological data from three-dimensional refractive index tomograms (providing measurements of cell volume, surface area, footprint, and sphericity at nanometer resolution, alongside dry mass and density).
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