Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients.

Tuan D Pham Vinayakumar RaviChuanwen FanBin LuoXiao-Feng Sun

Published in: IEEE journal of translational engineering in health and medicine (2022)

The ability to predict the survival rate of cancer patients is extremely important for clinical decision-making. The proposed AI tool is promising for assisting oncologists in their treatments of rectal cancer patients.

Keyphrases

deep learning
decision making
rectal cancer
artificial intelligence
machine learning
free survival
convolutional neural network
optical coherence tomography
neural network
palliative care
advanced cancer