3D axial-attention for lung nodule classification
M Al-Shabi, K Shak, M Tan - … journal of computer assisted radiology and …, 2021 - Springer
Abstract Purpose In recent years, Non-Local-based methods have been successfully
applied to lung nodule classification. However, these methods offer 2D attention or limited …
applied to lung nodule classification. However, these methods offer 2D attention or limited …
Semi-supervised graph learning framework for apicomplexan parasite classification
Y Ha, X Meng, Z Du, J Tian, Y Yuan - Biomedical Signal Processing and …, 2023 - Elsevier
Apicomplexan parasites cause diseases including malaria, toxoplasmosis, and babesiosis,
affecting large parts of the world and hampering economic development to a considerable …
affecting large parts of the world and hampering economic development to a considerable …
Advancing brain metastases detection in T1-weighted contrast-enhanced 3D MRI using noisy student-based training
The detection of brain metastases (BM) in their early stages could have a positive impact on
the outcome of cancer patients. The authors previously developed a framework for detecting …
the outcome of cancer patients. The authors previously developed a framework for detecting …
A Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology Images
FA Cano Ramirez - repositorio.unal.edu.co
Lung cancer is the second most common type and the leading cause of cancer death in the
world. It is divided into different types according to cellular and tissular features, and in turn …
world. It is divided into different types according to cellular and tissular features, and in turn …
[引用][C] Lung Cancer Disease Prediction using Artificial Intelligence: A Systematic Review
S Khoria, R Singh - Lung Cancer