# Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift

arXiv: https://arxiv.org/abs/2107.05938 · HTML: https://mlsanity.com/paper/2107.05938/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 13 Jul 2021.

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

- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [Unsupervised domain adaptation in brain lesion segmentation with adversarial networks](https://mlsanity.com/paper/1612.08894/) (2016)
- [Unpaired Multi-modal Segmentation via Knowledge Distillation](https://mlsanity.com/paper/2001.03111/) (2020)
- [Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT](https://mlsanity.com/paper/2009.02577/) (2020)
- [Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities](https://mlsanity.com/paper/1704.03379/) (2017)
- [Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets](https://mlsanity.com/paper/2009.04009/) (2020)
- [Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets](https://mlsanity.com/paper/1903.03445/) (2019)
- [NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines](https://mlsanity.com/paper/1806.04224/) (2018)

## Built on by (most cited first, 3 of 3)

- [MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation](https://mlsanity.com/paper/2303.14444/) (2023)
- [Multi-center anatomical segmentation with heterogeneous labels via landmark-based models](https://mlsanity.com/paper/2211.07395/) (2022)
- [ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment](https://mlsanity.com/paper/2607.05009/) (2026)

## Abstract (arXiv)

Scarcity of high quality annotated images remains a limiting factor for training accurate image segmentation models. While more and more annotated datasets become publicly available, the number of samples in each individual database is often small. Combining different databases to create larger amounts of training data is appealing yet challenging due to the heterogeneity as a result of differences in data acquisition and annotation processes, often yielding incompatible or even conflicting information. In this paper, we investigate and propose several strategies for learning from partially overlapping labels in the context of abdominal organ segmentation. We find that combining a semi-supervised approach with an adaptive cross entropy loss can successfully exploit heterogeneously annotated data and substantially improve segmentation accuracy compared to baseline and alternative approaches.