Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2020.
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Citing papers per year: 2021: 1; 2022: 4.
5 papers.
- 2022Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classificationcited by 5
- 2022Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shiftcited by 3
- 2021Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentationcited by 1
- 2022Object-Aware Self-supervised Multi-Label Learningcited by 1
- 2022Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifierscited by 1
- 2022Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification6 Sep 2022
- 2022Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifiers24 Aug 2022
- 2022Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift31 Jul 2022
- 2022Object-Aware Self-supervised Multi-Label Learning14 May 2022
- 2021Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation15 Aug 2021