# WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy

arXiv: https://arxiv.org/abs/2210.13109 · HTML: https://mlsanity.com/paper/2210.13109/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1607.03516/) (2016)
- [Micro-Net: A unified model for segmentation of various objects in microscopy images](https://mlsanity.com/paper/1804.08145/) (2018)
- [Domain Adaptive Semantic Segmentation Using Weak Labels](https://mlsanity.com/paper/2007.15176/) (2020)
- [Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images](https://mlsanity.com/paper/2007.05448/) (2020)
- [Scribble-based Domain Adaptation via Co-segmentation](https://mlsanity.com/paper/2007.03632/) (2020)
- [Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation](https://mlsanity.com/paper/2103.04705/) (2021)
- [ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2006.08658/) (2020)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Domain Adaptive Segmentation in Volume Electron Microscopy Imaging](https://mlsanity.com/paper/1810.09734/) (2018)
- [HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images](https://mlsanity.com/paper/2101.02877/) (2021)
- [Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision](https://mlsanity.com/paper/2108.08432/) (2021)

## Built on by (most cited first, 5 of 5)

- [Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation](https://mlsanity.com/paper/2404.00667/) (2024)
- [Prompt-DAS: Annotation-Efficient Prompt Learning for Domain Adaptive Semantic Segmentation of Electron Microscopy Images](https://mlsanity.com/paper/2509.18973/) (2025)
- [Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy](https://mlsanity.com/paper/2510.16450/) (2025)
- [Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy](https://mlsanity.com/paper/2602.19423/) (2026)
- [μMatch: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM](https://mlsanity.com/paper/2606.21605/) (2026)

## Abstract (arXiv)

Accurate segmentation of organelle instances, e.g., mitochondria, is essential for electron microscopy analysis. Despite the outstanding performance of fully supervised methods, they highly rely on sufficient per-pixel annotated data and are sensitive to domain shift. Aiming to develop a highly annotation-efficient approach with competitive performance, we focus on weakly-supervised domain adaptation (WDA) with a type of extremely sparse and weak annotation demanding minimal annotation efforts, i.e., sparse point annotations on only a small subset of object instances. To reduce performance degradation arising from domain shift, we explore multi-level transferable knowledge through conducting three complementary tasks, i.e., counting, detection, and segmentation, constituting a task pyramid with different levels of domain invariance. The intuition behind this is that after investigating a related source domain, it is much easier to spot similar objects in the target domain than to delineate their fine boundaries. Specifically, we enforce counting estimation as a global constraint to the detection with sparse supervision, which further guides the segmentation. A cross-position cut-and-paste augmentation is introduced to further compensate for the annotation sparsity. Extensive validations show that our model with only 15% point annotations can achieve comparable performance as supervised models and shows robustness to annotation selection.