Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Oral cancer detection and interpretation: Deep multiple instance
  learning versus conventional deep single instance learning   The current medical standard for setting an oral cancer (OC) diagnosis is
histological examination of a tissue sample from the oral cavity. This process
is time consuming and more invasive than an alternative approach of acquiring a
brush sample followed by cytological analysis. Skilled cytotechnologists are
able to detect changes due to malignancy, however, to introduce this approach
into clinical routine is associated with challenges such as a lack of experts
and labour-intensive work. To design a trustworthy OC detection system that
would assist cytotechnologists, we are interested in AI-based methods that
reliably can detect cancer given only per-patient labels (minimizing annotation
bias), and also provide information on which cells are most relevant for the
diagnosis (enabling supervision and understanding). We, therefore, perform a
comparison of a conventional single instance learning (SIL) approach and a
modern multiple instance learning (MIL) method suitable for OC detection and
interpretation, utilizing three different neural network architectures. To
facilitate systematic evaluation of the considered approaches, we introduce a
synthetic PAP-QMNIST dataset, that serves as a model of OC data, while offering
access to per-instance ground truth. Our study indicates that on PAP-QMNIST,
the SIL performs better, on average, than the MIL approach. Performance at the
bag level on real-world cytological data is similar for both methods, yet the
single instance approach performs better on average. Visual examination by
cytotechnologist indicates that the methods manage to identify cells which
deviate from normality, including malignant cells as well as those suspicious
for dysplasia. We share the code as open source at
https://github.com/MIDA-group/OralCancerMILvsSIL

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The categories of this paper are: stat
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
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The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph