# Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences

arXiv: https://arxiv.org/abs/2208.04524 · HTML: https://mlsanity.com/paper/2208.04524/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 92 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 16 of 16)

- [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)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification](https://mlsanity.com/paper/1602.02068/) (2016)
- [Rethinking the Value of Labels for Improving Class-Imbalanced Learning](https://mlsanity.com/paper/2006.07529/) (2020)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Revisiting Multiple Instance Neural Networks](https://mlsanity.com/paper/1610.02501/) (2016)
- [Modern Hopfield Networks and Attention for Immune Repertoire Classification](https://mlsanity.com/paper/2007.13505/) (2020)
- [Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis](https://mlsanity.com/paper/1711.09645/) (2017)
- [Convolutional neural network models for cancer type prediction based on gene expression](https://mlsanity.com/paper/1906.07794/) (2019)
- [Active Learning for Decision-Making from Imbalanced Observational Data](https://mlsanity.com/paper/1904.05268/) (2019)
- [Multiple Instance Learning Convolutional Neural Networks for Object Recognition](https://mlsanity.com/paper/1610.03155/) (2016)
- [Multiple Instance Learning with Bag Dissimilarities](https://mlsanity.com/paper/1309.5643/) (2013)
- [An embarrassingly simple approach to neural multiple instance classification](https://mlsanity.com/paper/1905.01947/) (2019)

## Built on by (most cited first, 2 of 2)

- [SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes](https://mlsanity.com/paper/2602.01051/) (2026)
- [DynImmune-BERT: Dynamic Immune Repertoire Modeling with Neural ODE Driven Continuous Transformers](https://mlsanity.com/paper/2607.17244/) (2026)

## Abstract (arXiv)

Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve (AUC) scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.