Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spectral Machine Learning for Pancreatic Mass Imaging Classification   We present a novel spectral machine learning (SML) method in screening for
pancreatic mass using CT imaging. Our algorithm is trained with approximately
30,000 images from 250 patients (50 patients with normal pancreas and 200
patients with abnormal pancreas findings) based on public data sources. A test
accuracy of 94.6 percents was achieved in the out-of-sample diagnosis
classification based on a total of approximately 15,000 images from 113
patients, whereby 26 out of 32 patients with normal pancreas and all 81
patients with abnormal pancreas findings were correctly diagnosed. SML is able
to automatically choose fundamental images (on average 5 or 9 images for each
patient) in the diagnosis classification and achieve the above mentioned
accuracy. The computational time is 75 seconds for diagnosing 113 patients in a
laptop with standard CPU running environment. Factors that influenced high
performance of a well-designed integration of spectral learning and machine
learning included: 1) use of eigenvectors corresponding to several of the
largest eigenvalues of sample covariance matrix (spike eigenvectors) to choose
input attributes in classification training, taking into account only the
fundamental information of the raw images with less noise; 2) removal of
irrelevant pixels based on mean-level spectral test to lower the challenges of
memory capacity and enhance computational efficiency while maintaining superior
classification accuracy; 3) adoption of state-of-the-art machine learning
classification, gradient boosting and random forest. Our methodology showcases
practical utility and improved accuracy of image diagnosis in pancreatic mass
screening in the era of AI.

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The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
14
The categories of this paper are: math-ph