# Annotation by Clicks: A Point-Supervised Contrastive Variance Method for Medical Semantic Segmentation

arXiv: https://arxiv.org/abs/2212.08774 · HTML: https://mlsanity.com/paper/2212.08774/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 36 references that are in the map.
- First posted on arXiv 17 Dec 2022.

## Builds on (most cited first, 28 of 28)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [Understanding the Behaviour of Contrastive Loss](https://mlsanity.com/paper/2012.09740/) (2020)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1604.05144/) (2016)
- [Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1803.10464/) (2018)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1503.01640/) (2015)
- [Simple Does It: Weakly Supervised Instance and Semantic Segmentation](https://mlsanity.com/paper/1603.07485/) (2016)
- [On Regularized Losses for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1803.09569/) (2018)
- [Mixed Transformer U-Net For Medical Image Segmentation](https://mlsanity.com/paper/2111.04734/) (2021)
- [Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision](https://mlsanity.com/paper/2203.02106/) (2022)
- [Constrained-CNN losses for weakly supervised segmentation](https://mlsanity.com/paper/1805.04628/) (2018)
- [Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images](https://mlsanity.com/paper/2007.05448/) (2020)
- [Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency](https://mlsanity.com/paper/2006.12890/) (2020)
- [Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation](https://mlsanity.com/paper/2109.07407/) (2021)
- [Learning to Segment Medical Images with Scribble-Supervision Alone](https://mlsanity.com/paper/1807.04668/) (2018)
- [Gated CRF Loss for Weakly Supervised Semantic Image Segmentation](https://mlsanity.com/paper/1906.04651/) (2019)
- [Going to Extremes: Weakly Supervised Medical Image Segmentation](https://mlsanity.com/paper/2009.11988/) (2020)
- [Mumford-Shah Loss Functional for Image Segmentation with Deep Learning](https://mlsanity.com/paper/1904.02872/) (2019)
- [Weakly Supervised Scene Parsing with Point-based Distance Metric Learning](https://mlsanity.com/paper/1811.02233/) (2018)
- [PseudoEdgeNet: Nuclei Segmentation only with Point Annotations](https://mlsanity.com/paper/1906.02924/) (2019)
- [A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images](https://mlsanity.com/paper/2007.02180/) (2020)
- [Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation](https://mlsanity.com/paper/2107.00583/) (2021)
- [Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2108.13393/) (2021)

## Built on by (most cited first, 4 of 4)

- [Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation](https://mlsanity.com/paper/2311.10696/) (2023)
- [Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency](https://mlsanity.com/paper/2309.09730/) (2023)
- [From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels](https://mlsanity.com/paper/2408.12814/) (2024)
- [Label tree semantic losses for rich multi-class medical image segmentation](https://mlsanity.com/paper/2507.15777/) (2025)

## Abstract (arXiv)

Medical image segmentation methods typically rely on numerous dense annotated images for model training, which are notoriously expensive and time-consuming to collect. To alleviate this burden, weakly supervised techniques have been exploited to train segmentation models with less expensive annotations. In this paper, we propose a novel point-supervised contrastive variance method (PSCV) for medical image semantic segmentation, which only requires one pixel-point from each organ category to be annotated. The proposed method trains the base segmentation network by using a novel contrastive variance (CV) loss to exploit the unlabeled pixels and a partial cross-entropy loss on the labeled pixels. The CV loss function is designed to exploit the statistical spatial distribution properties of organs in medical images and their variance distribution map representations to enforce discriminative predictions over the unlabeled pixels. Experimental results on two standard medical image datasets demonstrate that the proposed method outperforms the state-of-the-art weakly supervised methods on point-supervised medical image semantic segmentation tasks.