# A Multi-Stage Optimization Pipeline for Bethesda Cell Detection in Pap Smear Cytology

arXiv: https://arxiv.org/abs/2604.13939 · HTML: https://mlsanity.com/paper/2604.13939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 15 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review](https://mlsanity.com/paper/2105.11277/) (2021)

## Abstract (arXiv)

Computer vision techniques have advanced significantly in recent years, finding diverse and impactful applications within the medical field. In this paper, we introduce a new framework for the detection of Bethesda cells in Pap smear images, developed for Track B of the Riva Cytology Challenge held in association with the International Symposium on Biomedical Imaging (ISBI). This work focuses on enhancing computer vision models for cell detection, with performance evaluated using the mAP50-95 metric. We propose a solution based on an ensemble of YOLO and U-Net architectures, followed by a refinement stage utilizing overlap removal techniques and a binary classifier. Our framework achieved second place with a mAP50-95 score of 0.5909 in the competition. The implementation and source code are available at the following repository: github.com/martinamster/riva-trackb