Machine learning in the positron emission tomography imaging of Alzheimer's disease
C Ayubcha, SB Singh, KH Patel… - Nuclear Medicine …, 2023 - journals.lww.com
The utilization of machine learning techniques in medicine has exponentially increased over
the last decades due to innovations in computer processing, algorithm development, and …
the last decades due to innovations in computer processing, algorithm development, and …
A-beta staining segmentation and quantification using U-Net-based approach
Y Chen - Proceedings of the 2024 6th International Conference …, 2024 - dl.acm.org
This study introduces novel U-Net models adept at semantically segmenting Aβ and DAPI
staining on immunostained mouse brain sections. Through manual annotations of Aβ …
staining on immunostained mouse brain sections. Through manual annotations of Aβ …
Comprehensive Review of AI Integration in Nuclear Medicine–Current Techniques, Limitations, and Future Innovations
A Garcia-Tejedor Bilbao-Goyoaga - 2024 - studenttheses.uu.nl
The integration of Artificial Intelligence (AI) methods in Positron Emission Tomography (PET)
and Single-Photon Emission Computed Tomography (SPECT) presents unique challenges …
and Single-Photon Emission Computed Tomography (SPECT) presents unique challenges …
[PDF][PDF] A Neural Model of Unsupervised Clustering: Application to Alzheimer's Disease FDG-PET images
M Timouyas, S Eddarouich - researchgate.net
This paper proposes a new unsupervised segmentation model based entirely on neural
networks. This region-based segmentation is realized by the clustering of pixels in RGB …
networks. This region-based segmentation is realized by the clustering of pixels in RGB …