# ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation

arXiv: https://arxiv.org/abs/2005.00328 · HTML: https://mlsanity.com/paper/2005.00328/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 13 of 13)

- [CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision](https://mlsanity.com/paper/2203.01475/) (2022)
- [ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation](https://mlsanity.com/paper/2206.02118/) (2022)
- [ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation](https://mlsanity.com/paper/2402.02029/) (2024)
- [ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding](https://mlsanity.com/paper/2307.16226/) (2023)
- [ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation](https://mlsanity.com/paper/2301.04882/) (2023)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning](https://mlsanity.com/paper/2108.11900/) (2021)
- [SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images](https://mlsanity.com/paper/2403.01381/) (2024)
- [ScribbleVS: Scribble-Supervised Medical Image Segmentation via Dynamic Competitive Pseudo Label Selection](https://mlsanity.com/paper/2411.10237/) (2024)
- [Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors](https://mlsanity.com/paper/2310.08084/) (2023)
- [SP 3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation](https://mlsanity.com/paper/2411.11636/) (2024)
- [HELPNet: Hierarchical Perturbations Consistency and Entropy-guided Ensemble for Scribble Supervised Medical Image Segmentation](https://mlsanity.com/paper/2412.18738/) (2024)
- [ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision](https://mlsanity.com/paper/2605.06266/) (2026)

## Abstract (arXiv)

We propose adversarial constrained-CNN loss, a new paradigm of constrained-CNN loss methods, for weakly supervised medical image segmentation. In the new paradigm, prior knowledge is encoded and depicted by reference masks, and is further employed to impose constraints on segmentation outputs through adversarial learning with reference masks. Unlike pseudo label methods for weakly supervised segmentation, such reference masks are used to train a discriminator rather than a segmentation network, and thus are not required to be paired with specific images. Our new paradigm not only greatly facilitates imposing prior knowledge on network's outputs, but also provides stronger and higher-order constraints, i.e., distribution approximation, through adversarial learning. Extensive experiments involving different medical modalities, different anatomical structures, different topologies of the object of interest, different levels of prior knowledge and weakly supervised annotations with different annotation ratios is conducted to evaluate our ACCL method. Consistently superior segmentation results over the size constrained-CNN loss method have been achieved, some of which are close to the results of full supervision, thus fully verifying the effectiveness and generalization of our method. Specifically, we report an average Dice score of 75.4% with an average annotation ratio of 0.65%, surpassing the prior art, i.e., the size constrained-CNN loss method, by a large margin of 11.4%. Our codes are made publicly available at https://github.com/PengyiZhang/ACCL.