Cargando…
Lung Nodule Malignancy Prediction From Longitudinal CT Scans With Siamese Convolutional Attention Networks
Goal: We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure. Methods: Our proposed framework is evaluated for single- and multi-time-point classification t...
Formato: | Online Artículo Texto |
---|---|
Lenguaje: | English |
Publicado: |
IEEE
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8975149/ https://www.ncbi.nlm.nih.gov/pubmed/35402947 http://dx.doi.org/10.1109/OJEMB.2020.3023614 |
Ejemplares similares
-
Highly accurate model for prediction of lung nodule malignancy with CT scans
por: Causey, Jason L., et al.
Publicado: (2018) -
Automated fundus ultrasound image classification based on siamese convolutional neural networks with multi-attention
por: Tan, Jiachen, et al.
Publicado: (2023) -
McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices
por: Ahuja, Sakshi, et al.
Publicado: (2022) -
Siamese Recurrent Neural Network with a Self-Attention
Mechanism for Bioactivity Prediction
por: Fernández-Llaneza, Daniel, et al.
Publicado: (2021) -
PET and CT Image Fusion of Lung Cancer With Siamese Pyramid Fusion Network
por: Xiao, Ning, et al.
Publicado: (2022)