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

arXiv: https://arxiv.org/abs/2202.01783 · HTML: https://mlsanity.com/paper/2202.01783/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Cold Case: The Lost MNIST Digits](https://mlsanity.com/paper/1905.10498/) (2019)
- [A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images](https://mlsanity.com/paper/1910.10549/) (2019)

## Built on by (most cited first, 1 of 1)

- [Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning](https://mlsanity.com/paper/2302.08867/) (2023)

## Abstract (arXiv)

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