# Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity

arXiv: https://arxiv.org/abs/2305.19069 · HTML: https://mlsanity.com/paper/2305.19069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 68 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [A review: Deep learning for medical image segmentation using multi-modality fusion](https://mlsanity.com/paper/2004.10664/) (2020)
- [Deep learning in ultrasound imaging](https://mlsanity.com/paper/1907.02994/) (2019)
- [Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning](https://mlsanity.com/paper/2109.05664/) (2021)
- [Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People](https://mlsanity.com/paper/2006.15940/) (2020)
- [Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification](https://mlsanity.com/paper/2106.08590/) (2021)
- [CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation](https://mlsanity.com/paper/2110.02417/) (2021)
- [BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis](https://mlsanity.com/paper/2004.03500/) (2020)

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

- [3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images](https://mlsanity.com/paper/2309.11015/) (2023)

## Abstract (arXiv)

Lesion segmentation of ultrasound medical images based on deep learning techniques is a widely used method for diagnosing diseases. Although there is a large amount of ultrasound image data in medical centers and other places, labeled ultrasound datasets are a scarce resource, and it is likely that no datasets are available for new tissues/organs. Transfer learning provides the possibility to solve this problem, but there are too many features in natural images that are not related to the target domain. As a source domain, redundant features that are not conducive to the task will be extracted. Migration between ultrasound images can avoid this problem, but there are few types of public datasets, and it is difficult to find sufficiently similar source domains. Compared with natural images, ultrasound images have less information, and there are fewer transferable features between different ultrasound images, which may cause negative transfer. To this end, a multi-source adversarial transfer learning network for ultrasound image segmentation is proposed. Specifically, to address the lack of annotations, the idea of adversarial transfer learning is used to adaptively extract common features between a certain pair of source and target domains, which provides the possibility to utilize unlabeled ultrasound data. To alleviate the lack of knowledge in a single source domain, multi-source transfer learning is adopted to fuse knowledge from multiple source domains. In order to ensure the effectiveness of the fusion and maximize the use of precious data, a multi-source domain independent strategy is also proposed to improve the estimation of the target domain data distribution, which further increases the learning ability of the multi-source adversarial migration learning network in multiple domains.