Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Nov 1, 2025
Radiomics and artificial intelligence (AI) are rapidly evolving, significantly transforming the field of medical imaging. Despite their growing adoption, these technologies remain challenging to approach due to their technical complexity. This review...
Humans segment experience into a nested series of discrete events, separated by neural state transitions that can be identified in fMRI data collected during passive movie viewing. Current neural state segmentation techniques manage the noisiness of ...
IEEE transactions on bio-medical engineering
Nov 1, 2025
A transfer function approach has recently proven effective for calibrating deep learning (DL) algorithms in quantitative ultrasound (QUS), addressing data shifts at both the acquisition and machine levels. Expanding on this approach, we develop a str...
Medical image classification is critical for accurate disease diagnosis, necessitating models that balance performance and interpretability. This study presents Dilated Y-Block-based Feature Summarized Pyramidal Attention Network (DY-FSPAN), a deep l...
IEEE transactions on bio-medical engineering
Oct 1, 2025
This study introduces a novel data-centric approach to improve real-time surgical guidance using fiber-based fluorescence lifetime imaging (FLIm). A key aspect of the methodology is the accurate detection of the aiming beam, which is essential for lo...
IEEE transactions on biomedical circuits and systems
Oct 1, 2025
The miniaturization and real time imaging capability have always been the desired properties of photoacoustic imaging (PAI) system, which unlocked vast potential for personalized healthcare and diagnostics. While the imaging quality and resolution in...
The digitization of natural history specimens has unlocked opportunities for large-scale phenotypic trait analysis. In recent years, deep learning has shown significant results in accurately predicting annotations on 2D specimen photographs. However,...
Deep learning methods using convolutional neural networks are very effective for automatic image segmentation tasks with no exception for cellular electron micrographs. However, the lack of dedicated easy-to-use tools largely reduces the widespread u...
Journal of applied clinical medical physics
Sep 1, 2025
PURPOSE: To explore the feasibility of transcatheter aortic valve implantation (TAVI) planning computed tomography (CT) on single-source 8-cm detector scanners with proper dose control by using two deep-learning reconstruction algorithms.
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