# MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images

arXiv: https://arxiv.org/abs/2010.14731 · HTML: https://mlsanity.com/paper/2010.14731/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Data](https://mlsanity.com/paper/2110.13185/) (2021)
- [Autoadaptive Medical Segment Anything Model](https://mlsanity.com/paper/2507.01828/) (2025)
- [EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgery](https://mlsanity.com/paper/2506.06830/) (2025)
- [Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learning](https://mlsanity.com/paper/2510.10068/) (2025)
- [3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework](https://mlsanity.com/paper/2109.13862/) (2021)
- [Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentation](https://mlsanity.com/paper/2505.19208/) (2025)

## Abstract (arXiv)

Semi-supervised learning via learning from limited quantities of labeled data has been investigated as an alternative to supervised counterparts. Maximizing knowledge gains from copious unlabeled data benefit semi-supervised learning settings. Moreover, learning multiple tasks within the same model further improves model generalizability. We propose a novel multitask learning model, namely MultiMix, which jointly learns disease classification and anatomical segmentation in a sparingly supervised manner, while preserving explainability through bridge saliency between the two tasks. Our extensive experimentation with varied quantities of labeled data in the training sets justify the effectiveness of our multitasking model for the classification of pneumonia and segmentation of lungs from chest X-ray images. Moreover, both in-domain and cross-domain evaluations across the tasks further showcase the potential of our model to adapt to challenging generalization scenarios.