AIMC Topic: Deep Learning
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PursuitNet: A deep learning model for predicting competitive pursuit-like behavior in mice.
Brain research
Apr 8, 2025
Predator-prey interactions exemplify adaptive intelligence refined by evolution, yet replicating these behaviors in artificial systems remains challenging. Here, we introduce PursuitNet, a deep learning framework specifically designed to model the co...
Advances in MRI optic nerve segmentation.
Multiple sclerosis and related disorders
Apr 8, 2025
Understanding optic nerve structure and monitoring changes within it can provide insights into neurodegenerative diseases like multiple sclerosis, in which optic nerves are often damaged by inflammatory episodes of optic neuritis. Over the past decad...
Aligning, Autoencoding and Prompting Large Language Models for Novel Disease Reporting.
IEEE transactions on pattern analysis and machine intelligence
Apr 8, 2025
Given radiology images, automatic radiology report generation aims to produce informative text that reports diseases. It can benefit current clinical practice in diagnostic radiology. Existing methods typically rely on large-scale medical datasets an...
Unified Deep Learning of Molecular and Protein Language Representations with T5ProtChem.
Journal of chemical information and modeling
Apr 8, 2025
Deep learning has revolutionized difficult tasks in chemistry and biology, yet existing language models often treat these domains separately, relying on concatenated architectures and independently pretrained weights. These approaches fail to fully e...
Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges.
Sensors (Basel, Switzerland)
Apr 8, 2025
The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image pro...
Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.
BMC oral health
Apr 8, 2025
BACKGROUND: Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft an...
MIDAA: deep archetypal analysis for interpretable multi-omic data integration based on biological principles.
Genome biology
Apr 8, 2025
High-throughput multi-omic molecular profiling allows the probing of biological systems at unprecedented resolution. However, integrating and interpreting high-dimensional, sparse, and noisy multimodal datasets remains challenging. Deriving new biolo...
Anesthesia depth prediction from drug infusion history using hybrid AI.
BMC medical informatics and decision making
Apr 8, 2025
BACKGROUND: Accurately predicting the depth of anesthesia is essential for ensuring patient safety and optimizing surgical outcomes. Traditional regression-based approaches often struggle to model the complex and dynamic nature of patient responses t...
DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations.
BMC bioinformatics
Apr 8, 2025
Gene expression is the basis for cells to achieve various functions, while DNA methylation constitutes a critical epigenetic mechanism governing gene expression regulation. Here we propose DeepMethyGene, an adaptive recursive convolutional neural net...
Leveraging artificial intelligence for diagnosis of children autism through facial expressions.
Scientific reports
Apr 8, 2025
The global population contains a substantial number of individuals who experience autism spectrum disorder, thus requiring immediate identification to enable successful intervention approaches. The authors assess the detection of autism-related learn...
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