Artificial Intelligence in Radiation Oncology

Fri, 15 Oct 2021 00:49:35 -0400

PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma

Thu, 22 Jul 2021 12:57:18 -0400

Jung Hun Oh, Wookjin Choi, Euiseong Ko, Mingon Kang, Allen Tannenbaum, Joseph O Deasy

The authors wish it to be known that, in their opinion, Jung Hun Oh and Wookjin Choi should be regarded as Joint First Authors.

https://academic.oup.com/bioinformatics/article/37/Supplement_1/i443/6319702

https://github.com/mskspi/PathCNN/raw/main/img/pathcnn.png

An illustration of biological interpretation. (A) Grad-CAM procedure to generate class activation maps. The two images on the left bottom represent an example of the class activation maps for a sample in the cohort, which were generated from Grad-CAM procedure; (B) statistical analysis to identify significantly different pathways between the LTS and non-LTS groups. LTS, long-term survival; CNN, convolutional neural network; ReLU, rectified linear unit

Cnn on Qualia Radiomics

Artificial Intelligence in Radiation Oncology

PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma