# Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling

arXiv: https://arxiv.org/abs/2203.04964 · HTML: https://mlsanity.com/paper/2203.04964/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [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)
- [Image biomarker standardisation initiative](https://mlsanity.com/paper/1612.07003/) (2016)
- [Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer](https://mlsanity.com/paper/1703.08516/) (2017)
- [From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities](https://mlsanity.com/paper/1808.07954/) (2018)
- [AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases](https://mlsanity.com/paper/2012.06875/) (2020)
- [Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification](https://mlsanity.com/paper/2107.07805/) (2021)

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

- [Cross-Validation Is All You Need: A Statistical Approach To Label Noise Estimation](https://mlsanity.com/paper/2306.13990/) (2023)

## Abstract (arXiv)

Cancer stage is a large determinant of patient prognosis and management in many cancer types, and is often assessed using medical imaging modalities, such as CT and MRI. These medical images contain rich information that can be explored to stratify patients within each stage group to further improve prognostic algorithms. Although the majority of cancer deaths result from metastatic and multifocal disease, building imaging biomarkers for patients with multiple tumors has been a challenging task due to the lack of annotated datasets and standard study framework. In this paper, we process two public datasets to set up a benchmark cohort of 341 patient in total for studying outcome prediction of multifocal metastatic cancer. We identify the lack of expressiveness in common multiple instance classification networks and propose two injective multiple instance pooling functions that are better suited to outcome prediction. Our results show that multiple instance learning with injective pooling functions can achieve state-of-the-art performance in the non-small-cell lung cancer CT and head and neck CT outcome prediction benchmarking tasks. We will release the processed multifocal datasets, our code and the intermediate files i.e. extracted radiomic features to support further transparent and reproducible research.