# Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification

arXiv: https://arxiv.org/abs/2209.02625 · HTML: https://mlsanity.com/paper/2209.02625/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks](https://mlsanity.com/paper/1904.10030/) (2019)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data](https://mlsanity.com/paper/2006.00080/) (2020)
- [A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection](https://mlsanity.com/paper/2102.07978/) (2021)
- [3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management](https://mlsanity.com/paper/2012.04701/) (2020)
- [Fast Multi-Instance Multi-Label Learning](https://mlsanity.com/paper/1310.2049/) (2013)
- [VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data](https://mlsanity.com/paper/2010.08357/) (2020)
- [Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma](https://mlsanity.com/paper/2005.04073/) (2020)
- [Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization](https://mlsanity.com/paper/1905.05061/) (2019)

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

- [Self-Paced Learning for Images of Antinuclear Antibodies](https://mlsanity.com/paper/2511.21519/) (2025)
- [CeCNN: Copula-enhanced convolutional neural networks in joint prediction of refraction error and axial length based on ultra-widefield fundus images](https://mlsanity.com/paper/2311.03967/) (2023)
- [OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images](https://mlsanity.com/paper/2403.11974/) (2024)
- [A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends](https://mlsanity.com/paper/2406.06119/) (2024)
- [OU-CoViT: Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF Images](https://mlsanity.com/paper/2408.09395/) (2024)

## Abstract (arXiv)

described by multiple instances (e.g., image patches) and simultaneously associated with multiple labels. Existing MIML methods are useful in many applications but most of which suffer from relatively low accuracy and training efficiency due to several issues: i) the inter-label correlations(i.e., the probabilistic correlations between the multiple labels corresponding to an object) are neglected; ii) the inter-instance correlations (i.e., the probabilistic correlations of different instances in predicting the object label) cannot be learned directly (or jointly) with other types of correlations due to the missing instance labels; iii) diverse inter-correlations (e.g., inter-label correlations, inter-instance correlations) can only be learned in multiple stages. To resolve these issues, a new single-stage framework called broad multi-instance multi-label learning (BMIML) is proposed. In BMIML, there are three innovative modules: i) an auto-weighted label enhancement learning (AWLEL) based on broad learning system (BLS) is designed, which simultaneously and efficiently captures the inter-label correlations while traditional BLS cannot; ii) A specific MIML neural network called scalable multi-instance probabilistic regression (SMIPR) is constructed to effectively estimate the inter-instance correlations using the object label only, which can provide additional probabilistic information for learning; iii) Finally, an interactive decision optimization (IDO) is designed to combine and optimize the results from AWLEL and SMIPR and form a single-stage framework. Experiments show that BMIML is highly competitive to (or even better than) existing methods in accuracy and much faster than most MIML methods even for large medical image data sets (> 90K images).