# Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection

arXiv: https://arxiv.org/abs/2104.03265 · HTML: https://mlsanity.com/paper/2104.03265/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2021.

## Built on by (most cited first, 6 of 6)

- [WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma](https://mlsanity.com/paper/2204.06455/) (2022)
- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [DoNet: Deep De-overlapping Network for Cytology Instance Segmentation](https://mlsanity.com/paper/2303.14373/) (2023)
- [Exploring Contextual Relationships for Cervical Abnormal Cell Detection](https://mlsanity.com/paper/2207.04693/) (2022)
- [Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images](https://mlsanity.com/paper/2306.13301/) (2023)
- [Holistic and Historical Instance Comparison for Cervical Cell Detection](https://mlsanity.com/paper/2409.13987/) (2024)

## Abstract (arXiv)

Deep learning has achieved unprecedented success in various object detection tasks with huge amounts of labeled data. However, obtaining large-scale annotations for medical images is extremely challenging due to the high demand of labour and expertise. In this paper, we propose a novel deep semi-supervised metric learning method to effectively leverage both labeled and unlabeled data for cervical cancer cell detection. Specifically, our model learns a metric space and conducts dual alignment of semantic features on both the proposal level and the prototype levels. On the proposal level, we align the unlabeled data with class proxies derived from the labeled data. We further align the prototypes of the labeled and unlabeled data to alleviate the influence of possibly noisy pseudo labels generated at the proposal alignment stage. Moreover, we adopt a memory bank to store the labeled prototypes, which significantly enrich the metric learning information from larger batches. Extensive experiments show our proposed method outperforms other state-of-the-art semi-supervised approaches consistently, demonstrating the efficacy of our proposed deep semi-supervised metric learning with dual alignment.