AIMC Topic: Neural Networks, Computer

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Comparative analysis of vision transformers and convolutional neural networks in osteoporosis detection from X-ray images.

Scientific reports
Within the scope of this investigation, we carried out experiments to investigate the potential of the Vision Transformer (ViT) in the field of medical image analysis. The diagnosis of osteoporosis through inspection of X-ray radio-images is a substa...

Estimating three-dimensional foot bone kinematics from skin markers using a deep learning neural network model.

Journal of biomechanics
The human foot is a complex structure comprising 26 bones, whose coordinated movements facilitate proper deformation of the foot, ensuring stable and efficient locomotion. Despite their critical role, the kinematics of foot bones during movement rema...

Adaptive neighborhood triplet loss: enhanced segmentation of dermoscopy datasets by mining pixel information.

International journal of computer assisted radiology and surgery
PURPOSE: The integration of deep learning in image segmentation technology markedly improves the automation capabilities of medical diagnostic systems, reducing the dependence on the clinical expertise of medical professionals. However, the accuracy ...

MIGP: Metapath Integrated Graph Prompt Neural Network.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) leveraging metapaths have garnered extensive utilization. Nevertheless, the escalating parameters and data corpus within graph pre-training models incur mounting training costs. Consequently, GNN models encounter hurdles ...

Towards a rigorous analysis of mutual information in contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Contrastive learning has emerged as a cornerstone in unsupervised representation learning. Its primary paradigm involves an instance discrimination task utilizing InfoNCE loss where the loss has been proven to be a form of mutual information. Consequ...

Towards key genes identification for breast cancer survival risk with neural network models.

Computational biology and chemistry
Breast cancer, one common malignant tumor all over the world, has a considerably high rate of recurrence, which endangers the health and life of patients. While more and more data have been available, how to leverage the gene expression data to predi...

Automatic diagnosis for adenomyosis in ultrasound images by deep neural networks.

European journal of obstetrics, gynecology, and reproductive biology
OBJECTIVE: To present a new noninvasive technique for automatic diagnosis of adenomyosis, using a novel end-to-end unified network framework based on transformer networks.

Structure and position-aware graph neural network for airway labeling.

Medical image analysis
We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are ex...

Identification of footstrike pattern using accelerometry and machine learning.

Journal of biomechanics
Recent reports have suggested that there may be a relationship between footstrike pattern and overuse injury incidence and type. With the recent increase in wearable sensors, it is important to identify paradigms where the footstrike pattern can be d...

GraphPro: An interpretable graph neural network-based model for identifying promoters in multiple species.

Computers in biology and medicine
Promoters are DNA sequences that bind with RNA polymerase to initiate transcription, regulating this process through interactions with transcription factors. Accurate identification of promoters is crucial for understanding gene expression regulation...