Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prediction of MGMT Methylation Status of Glioblastoma using Radiomics
  and Latent Space Shape Features   In this paper we propose a method for predicting the status of MGMT promoter
methylation in high-grade gliomas. From the available MR images, we segment the
tumor using deep convolutional neural networks and extract both radiomic
features and shape features learned by a variational autoencoder. We
implemented a standard machine learning workflow to obtain predictions,
consisting of feature selection followed by training of a random forest
classification model. We trained and evaluated our method on the
RSNA-ASNR-MICCAI BraTS 2021 challenge dataset and submitted our predictions to
the challenge.

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The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math