AIMC Topic: Deep Learning
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An interpretable deep learning approach for autism spectrum disorder detection in children using NASNet-mobile.
Biomedical physics & engineering express
Jun 3, 2025
Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder featuring impaired social interactions and communication abilities engaging the individuals in a restrictive or repetitive behaviour. Though incurable early detection and in...
Enhancing Lesion Detection in Inflammatory Myelopathies: A Deep Learning-Reconstructed Double Inversion Recovery MRI Approach.
AJNR. American journal of neuroradiology
Jun 3, 2025
BACKGROUND AND PURPOSE: The imaging of inflammatory myelopathies has advanced significantly across time, with MRI techniques playing a pivotal role in enhancing lesion detection. However, the impact of deep learning (DL)-based reconstruction on 3D do...
DeepSurv-based deep learning model for survival prediction and personalized treatment recommendation in tongue squamous cell carcinoma.
Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Jun 2, 2025
We developed a DeepSurv-based deep neural network for survival prediction and treatment recommendation in tongue squamous cell carcinoma (TSCC). The model was trained on 2,015 patients from the Surveillance, Epidemiology, and End Results (SEER) datab...
Accelerating 3D radial MPnRAGE using a self-supervised deep factor model.
Magnetic resonance in medicine
Jun 2, 2025
PURPOSE: To develop a self-supervised and memory-efficient deep learning image reconstruction method for 4D non-Cartesian MRI with high resolution and a large parametric dimension.
A Scalable Deep Learning Approach for Real-Time Multivariate Monitoring of Biopharmaceutical Processes With No Prior Product-Specific History.
Biotechnology and bioengineering
Jun 2, 2025
Real-time multivariate statistical process monitoring (RT-MSPM) is essential to monitor health of bio-pharmaceutical processes and detect anomalies and faults early in the process. RT-MSPM methods are commonly used to monitor cell culture process ope...
NPI-HetGNN: A Prediction Model of ncRNA-Protein Interactions Based on Heterogeneous Graph Neural Networks.
Interdisciplinary sciences, computational life sciences
Jun 2, 2025
Non-coding RNAs (ncRNAs) are one of the components of epigenetic mechanisms that regulates gene expression. Studying ncRNA-protein interactions (NPI) can help to explore a wide range of biological features and related diseases. Traditional NPI resear...
SASWISE-UE: Segmentation and synthesis with interpretable scalable ensembles for uncertainty estimation.
Computers in biology and medicine
Jun 2, 2025
This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to...
ViTU-net: A hybrid deep learning model with patch-based LSB approach for medical image watermarking and authentication using a hybrid metaheuristic algorithm.
Computers in biology and medicine
Jun 2, 2025
In modern healthcare, telemedicine, health records, and AI-driven diagnostics depend on medical image watermarking to secure chest X-rays for pneumonia diagnosis, ensuring data integrity, confidentiality, and authenticity. A 2024 study found over 70 ...
Direct parametric reconstruction in dynamic PET using deep image prior and a novel parameter magnification strategy.
Computers in biology and medicine
Jun 2, 2025
BACKGROUND/PURPOSE: Multiple parametric imaging in positron emission tomography (PET) is challenging due to the noisy dynamic data and the complex mapping to kinetic parameters. Although methods like direct parametric reconstruction have been propose...
Deep learning approaches to surgical video segmentation and object detection: A scoping review.
Computers in biology and medicine
Jun 2, 2025
INTRODUCTION: Computer vision (CV) has had a transformative impact in biomedical fields such as radiology, dermatology, and pathology. Its real-world adoption in surgical applications, however, remains limited. We review the current state-of-the-art ...
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