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Revolutionizing Prostate Cancer Detection: The Role of Approved PSMA-PET Imaging Agents. [PDF]

Pharmaceuticals (Basel)
Hennrich U, Wagner L, Taş H, Kovacs L, Benešová-Schäfer M.   +4 more
europepmc   +1 more source

U-Net benign prostatic hyperplasia-trained deep learning model for prostate ultrasound image segmentation in prostate cancer. [PDF]

Quant Imaging Med Surg
Jiao P, Wang Z, Chen Z, Weng X, Liu X, Yang R, Zheng Q.   +6 more
europepmc   +1 more source

A cross-sectional study examining the relationship between the advanced lung cancer inflammation index and prostate cancer. [PDF]

J Health Popul Nutr
Huang M, Teng Q, Ning D, Tong T, Cao F, Wang Y, Lei H, Pang J.   +7 more
europepmc   +1 more source

Perfusion imaging parameters predict long-term clinical outcome in isolated posterior cerebral artery occlusion stroke patients.

Interv Neuroradiol
Salim HA, Lakhani DA, Me J, Balar A, Huang S, Hoseinyazdi M, Luna L, Deng F, Hyson NZ, Bahouth M, Dmytriw AA, Guenego A, Albers GW, Lu H, Urrutia VC, Nael K, Marsh EB, Hillis AE, Llinas R, Wintermark M, Heit JJ, Faizy TD, Yedavalli V.   +22 more
europepmc   +1 more source

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Performance Analysis of PCA, Sparse PCA, Kernel PCA and Incremental PCA Algorithms for Heart Failure Prediction

2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2020
Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction.
Atiqur Rehman, Aurangzeb Khan, Muhammad Akhtar Ali, Muhammad Umair Khan, S. Khan, Liaqat Ali   +5 more
semanticscholar   +3 more sources

Two-Dimensional Quaternion PCA and Sparse PCA

IEEE Transactions on Neural Networks and Learning Systems, 2019
Benefited from quaternion representation that is able to encode the cross-channel correlation of color images, quaternion principle component analysis (QPCA) was proposed to extract features from color images while reducing the feature dimension.
Xiaolin Xiao, Yicong Zhou
semanticscholar   +4 more sources