# Scribble-based Domain Adaptation via Co-segmentation

arXiv: https://arxiv.org/abs/2007.03632 · HTML: https://mlsanity.com/paper/2007.03632/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 30 of 34)

- [CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2201.02831/) (2022)
- [Source-Free Domain Adaptation for Image Segmentation](https://mlsanity.com/paper/2108.03152/) (2021)
- [Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation](https://mlsanity.com/paper/2107.00583/) (2021)
- [COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training](https://mlsanity.com/paper/2203.16557/) (2022)
- [Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures](https://mlsanity.com/paper/2208.08605/) (2022)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Challenges for machine learning in clinical translation of big data imaging studies](https://mlsanity.com/paper/2107.05630/) (2021)
- [Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations](https://mlsanity.com/paper/2205.06779/) (2022)
- [SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation](https://mlsanity.com/paper/2305.11012/) (2023)
- [Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation](https://mlsanity.com/paper/2210.10956/) (2022)
- [Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2202.03501/) (2022)
- [Cross-modal tumor segmentation using generative blending augmentation and self training](https://mlsanity.com/paper/2304.01705/) (2023)
- [SimPLe: Similarity-Aware Propagation Learning for Weakly-Supervised Breast Cancer Segmentation in DCE-MRI](https://mlsanity.com/paper/2306.16714/) (2023)
- [LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification](https://mlsanity.com/paper/2408.10069/) (2024)
- [Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2109.10674/) (2021)
- [Unsupervised Cross-Modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model Ensemble](https://mlsanity.com/paper/2109.12169/) (2021)
- [Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion](https://mlsanity.com/paper/2201.10647/) (2022)
- [WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy](https://mlsanity.com/paper/2210.13109/) (2022)
- [Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2303.14998/) (2023)
- [Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning](https://mlsanity.com/paper/2108.11900/) (2021)
- [Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2209.11879/) (2022)
- [MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2303.15826/) (2023)
- [Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation](https://mlsanity.com/paper/2404.00667/) (2024)
- [Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models](https://mlsanity.com/paper/2404.13239/) (2024)
- [Weakly Supervised Volumetric Image Segmentation with Deformed Templates](https://mlsanity.com/paper/2106.03987/) (2021)
- [Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2109.06274/) (2021)
- [Interactive Image Selection and Training for Brain Tumor Segmentation Network](https://mlsanity.com/paper/2406.03225/) (2024)
- [Deep Compatible Learning for Partially-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2206.09148/) (2022)
- [Scribble-Guided Diffusion for Training-free Text-to-Image Generation](https://mlsanity.com/paper/2409.08026/) (2024)
- [An Unpaired Cross-modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea](https://mlsanity.com/paper/2211.14986/) (2022)

## Abstract (arXiv)

Although deep convolutional networks have reached state-of-the-art performance in many medical image segmentation tasks, they have typically demonstrated poor generalisation capability. To be able to generalise from one domain (e.g. one imaging modality) to another, domain adaptation has to be performed. While supervised methods may lead to good performance, they require to fully annotate additional data which may not be an option in practice. In contrast, unsupervised methods don't need additional annotations but are usually unstable and hard to train. In this work, we propose a novel weakly-supervised method. Instead of requiring detailed but time-consuming annotations, scribbles on the target domain are used to perform domain adaptation. This paper introduces a new formulation of domain adaptation based on structured learning and co-segmentation. Our method is easy to train, thanks to the introduction of a regularised loss. The framework is validated on Vestibular Schwannoma segmentation (T1 to T2 scans). Our proposed method outperforms unsupervised approaches and achieves comparable performance to a fully-supervised approach.