# A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision

arXiv: https://arxiv.org/abs/2107.08394 · HTML: https://mlsanity.com/paper/2107.08394/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Positive-Unlabeled Learning with Non-Negative Risk Estimator](https://mlsanity.com/paper/1703.00593/) (2017)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [Class-prior Estimation for Learning from Positive and Unlabeled Data](https://mlsanity.com/paper/1611.01586/) (2016)
- [Classification from Positive, Unlabeled and Biased Negative Data](https://mlsanity.com/paper/1810.00846/) (2018)
- [Self-Supervised Learning for Spinal MRIs](https://mlsanity.com/paper/1708.00367/) (2017)
- [Introducing Geometry in Active Learning for Image Segmentation](https://mlsanity.com/paper/1508.04955/) (2015)
- [Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation](https://mlsanity.com/paper/1608.03235/) (2016)
- [Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems](https://mlsanity.com/paper/2003.06258/) (2020)
- [Iterative multi-path tracking for video and volume segmentation with sparse point supervision](https://mlsanity.com/paper/1809.00970/) (2018)
- [Expected exponential loss for gaze-based video and volume ground truth annotation](https://mlsanity.com/paper/1707.04905/) (2017)
- [Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data](https://mlsanity.com/paper/1809.05710/) (2018)
- [Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation](https://mlsanity.com/paper/2010.01916/) (2020)

## Built on by (most cited first, 1 of 1)

- [Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation](https://mlsanity.com/paper/2304.07519/) (2023)

## Abstract (arXiv)

The ability to quickly annotate medical imaging data plays a critical role in training deep learning frameworks for segmentation. Doing so for image volumes or video sequences is even more pressing as annotating these is particularly burdensome. To alleviate this problem, this work proposes a new method to efficiently segment medical imaging volumes or videos using point-wise annotations only. This allows annotations to be collected extremely quickly and remains applicable to numerous segmentation tasks. Our approach trains a deep learning model using an appropriate Positive/Unlabeled objective function using sparse point-wise annotations. While most methods of this kind assume that the proportion of positive samples in the data is known a-priori, we introduce a novel self-supervised method to estimate this prior efficiently by combining a Bayesian estimation framework and new stopping criteria. Our method iteratively estimates appropriate class priors and yields high segmentation quality for a variety of object types and imaging modalities. In addition, by leveraging a spatio-temporal tracking framework, we regularize our predictions by leveraging the complete data volume. We show experimentally that our approach outperforms state-of-the-art methods tailored to the same problem.