Oral cancer detection and interpretation: Deep multiple instance learning versus conventional deep single instance learning

• URL: http://arxiv.org/abs/2202.01783v1
• Date: Thu, 3 Feb 2022 15:04:26 GMT
• Title: Oral cancer detection and interpretation: Deep multiple instance learning versus conventional deep single instance learning
• Authors: Nadezhda Koriakina, Nata\v{s}a Sladoje, Vladimir Ba\v{s}i\'c and Joakim Lindblad
• Abstract summary: Current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample from the oral cavity. To introduce this approach into clinical routine is associated with challenges such as a lack of experts and labour-intensive work. We are interested in AI-based methods that reliably can detect cancer given only per-patient labels.
• Score: 2.2612425542955292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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