AIMC Topic: Neural Networks, Computer

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EmiNet: Moving bacteria detection on optical endomicroscopy images trained on synthetic data.

Computers in biology and medicine
Pneumonia, a respiratory disease often caused by bacterial infection in the distal lung, necessitates prompt and precise diagnosis, particularly in critical care settings. Optical endomicroscopy (OEM) facilitates real-time acquisition of in vivo and ...

Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions.

Biomedical engineering online
Understanding Cytochrome P450 (CYP) enzyme-mediated metabolism is critical for accurate Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions, which play a pivotal role in drug discovery. Traditional approaches, while foun...

Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble.

Scientific reports
Diabetes is a chronic condition brought on by either an inability to use insulin effectively or a lack of insulin produced by the body. If left untreated, this illness can be lethal to a person. Diabetes can be treated and a good life can be led with...

A new framework for mental illnesses diagnosis using wearable devices aided by improved convolutional neural network.

Scientific reports
Stress inherent in the modern world is considered one of the main causes of Mental Health Disorders (MHDs) that spread in every country around the world. These mental and behavioral problems primarily affect the mind and brain that change emotions an...

Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks.

Nature communications
Recurrent neural circuits often face inherent complexities in learning and generating their desired outputs, especially when they initially exhibit chaotic spontaneous activity. While the celebrated FORCE learning rule can train chaotic recurrent net...

Recent advances in applying machine learning to proton radiotherapy.

Biomedical physics & engineering express
.: In radiation oncology, precision and timeliness of both planning and treatment are paramount values of patient care. Machine learning has increasingly been applied to various aspects of photon radiotherapy to reduce manual error and improve the ef...

Analysis of interactions of particle-associated oxidative potential sources using multilayer perceptron neural networks: A case study in Shenyang, China.

Environmental pollution (Barking, Essex : 1987)
The oxidative potential (OP) of particulate matter (PM) is a possible indicator for assessing the oxidative-imbalance risk caused by PM exposure. The OP contributions of different PM sources exhibit nonlinear relationships, and the specific patterns ...

Multi-scale interaction and locally enhanced bridging network for medical image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate organ segmentation is crucial for precise medical diagnosis. Recent methods in CNNs and Transformers have significantly enhanced automatic medical image segmentation. Their encoders and decoders often rely on simple skip connections, which f...

SNA-SKAN: Unpaired learning for SDOCT speckle noise removal based on self noise assist and kolmogorov-arnold network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Optical Coherence Tomography (OCT) will inevitably be contaminated by speckle noise when imaging, resulting in a decrease in the visual quality of images and affecting clinical diagnosis. Existing unsupervised denoising methods often rely on complex ...

m5U-HybridNet: Integrating an RNA Foundation Model with CNN Features for Accurate Prediction of 5-Methyluridine Modification Sites.

Journal of chemical information and modeling
The 5-methyluridine (m5U) modification in RNA is vital for numerous biological processes, making its precise identification a key focus in computational biology. However, traditional wet-lab detection methods are cumbersome and time-consuming, wherea...