Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Fast Deep Learning Approach for Beam Orientation Optimization for
  Prostate Cancer IMRT Treatments   We propose a fast beam orientation selection method, based on deep neural
networks (DNN), capable of developing a plan comparable to those by the
state-of-the-art column generation method. The novelty of Our model lies in its
supervised learning structure, the DNN architecture, and ability to learn from
anatomical features to predict dosimetrically suitable beam orientations
without using the dosimetric information from the candidate beams, a time
consuming and computationally expensive process. This may save hours of
computation. A supervised DNN is trained to mimic the column generation
algorithm, which iteratively chooses beam orientations by calculating beam
fitness values based on the KKT optimality conditions. The dataset contains 70
prostate cancer patients. The DNN trained over 400 epochs, each with 2500
steps, using the Adam optimizer and a 6-fold cross-validation technique. The
average and standard deviation of training, validation, and testing loss
functions among the 6-folds were at most 1.44%. The differences in the dose
coverage of PTV between plans generated by column generation and by DNN were
0.2%. The average dose differences received by organs at risk were between 1
and 6 percent: bladder had the smallest average difference, then rectum, left
and right femoral heads. The dose received by body had an average difference of
0.1%. In the training phase of the proposed method, the model learns the
suitable beam orientations based on the anatomical features of patients and
omits time intensive calculations of dose influence matrices for all possible
candidate beams. Solving the Fluence Map Optimization to get the final
treatment plan requires calculating dose influence matrices only for the
selected beams. The proposed DNN is a fast beam orientation selection method
based that selects beam orientations in seconds and is therefore suitable for
clinical routines.

12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol