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Age Estimation Based on Graph Convolutional Networks and Multi-head Attention Mechanisms
The Graph Convolutional Network (GCN) is used to extract features from irregular face images effectively, and multi-head attention mechanisms are added to avoid redundant features and capture key region information in the image.
A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations
In addition, compared to the published models for genetic alterations, AMIML provided a significant improvement for predicting a wide range of genes (e. g., KMT2C, TP53, and SETD2 for KIRC; ERBB2, BRCA1, and BRCA2 for BRCA; JAK1, POLE, and MTOR for UCEC) as well as produced outstanding predictive models for other clinically relevant gene mutations, which have not been reported in the current literature.
Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering
We showed that unsupervised clustering of image patches could help identify predictive patches, exclude patches lack of predictive information, and therefore improve prediction on gene mutations in all three different cancer types, compared with the WSI based method without selection of image patches and models based on only tumor regions.
