AIMC Topic: Neural Networks, Computer

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Global contextual representation via graph-transformer fusion for hepatocellular carcinoma prognosis in whole-slide images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Current methods of digital pathological images typically employ small image patches to learn local representative features to overcome the issues of computationally heavy and memory limitations. However, the global contextual features are not fully c...

CardSegNet: An adaptive hybrid CNN-vision transformer model for heart region segmentation in cardiac MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiovascular MRI (CMRI) is a non-invasive imaging technique adopted for assessing the blood circulatory system's structure and function. Precise image segmentation is required to measure cardiac parameters and diagnose abnormalities through CMRI da...

Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand.

Environmental research
Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore t...

Magnetic Resonance Imaging Images Based Brain Tumor Extraction, Segmentation and Detection Using Convolutional Neural Network and VGC 16 Model.

American journal of clinical oncology
OBJECTIVES: In this paper, we look at how to design and build a system to find tumors using 2 Convolutional Neural Network (CNN) models. With the help of digital image processing and deep Learning, we can make a system that automatically diagnoses an...

Prediction of anti-cancer drug synergy based on cross-matching network and cancer molecular subtypes.

Computers in biology and medicine
At present, anti-cancer drug synergy therapy is one of the most important methods to overcome drug resistance and reduce drug toxicity in cancer treatment. High-throughput screening through deep learning can effectively improve the efficiency of disc...

Application of ionic liquid ultrasound-assisted extraction (IL-UAE) of lycopene from guava (Psidium guajava L.) by response surface methodology and artificial neural network-genetic algorithm.

Ultrasonics sonochemistry
Lycopene-rich guava (Psidium guajava L.) exhibits significant economic potential as a functional food ingredient, making it highly valuable for the pharmaceutical and agro-food industries. However, there is a need to enhance the extraction methods of...

Biomolecular Adsorption on Nanomaterials: Combining Molecular Simulations with Machine Learning.

Journal of chemical information and modeling
Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical m...

Sample self-selection using dual teacher networks for pathological image classification with noisy labels.

Computers in biology and medicine
Deep neural networks (DNNs) involve advanced image processing but depend on large quantities of high-quality labeled data. The presence of noisy data significantly degrades the DNN model performance. In the medical field, where model accuracy is cruc...

Implementation of a High-Accuracy Neural Network-Based Pupil Detection System for Real-Time and Real-World Applications.

Sensors (Basel, Switzerland)
In this paper, the implementation of a new pupil detection system based on artificial intelligence techniques suitable for real-time and real-word applications is presented. The proposed AI-based pupil detection system uses a classifier implemented w...

Analyzing to discover origins of CNNs and ViT architectures in medical images.

Scientific reports
In this paper, we introduce in-depth the analysis of CNNs and ViT architectures in medical images, with the goal of providing insights into subsequent research direction. In particular, the origins of deep neural networks should be explainable for me...