# Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma

arXiv: https://arxiv.org/abs/2005.04073 · HTML: https://mlsanity.com/paper/2005.04073/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 5 of 5)

- [Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification](https://mlsanity.com/paper/2209.02625/) (2022)
- [Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift](https://mlsanity.com/paper/2208.00465/) (2022)
- [Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation](https://mlsanity.com/paper/2108.06761/) (2021)
- [Object-Aware Self-supervised Multi-Label Learning](https://mlsanity.com/paper/2205.07028/) (2022)
- [Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifiers](https://mlsanity.com/paper/2209.03761/) (2022)

## Abstract (arXiv)

Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle this problem with multi-instance multi-label learning to address the difficulties on label correlations, label representations, etc. Furthermore, an effective oversampling strategy is applied for data imbalance. Experimental results have shown the superiority of the proposed approach.